How To Build a CX Program And Transform Your Business

Publié le par hanswillertbleuazurconsultingeu

Customer Experience (CX) is a catchy business term that has been used for decades, and until recently, measuring and managing it was not possible. Now, with the evolution of technology, a company can build and operationalize a true CX program.

For years, companies championed NPS surveys, CSAT scores, web feedback, and other sources of data as the drivers of “Customer Experience” – however, these singular sources of data don’t give a true, comprehensive view of how customers feel, think, and act. Unfortunately, most companies aren’t capitalizing on the benefits of a CX program. Less than 10% of companies have a CX executive and of those companies, only 14% believe Customer Experience, as a program, is the aggregation and analysis of all customer interactions with the objective of uncovering and disseminating insights across the company in order to improve the experience. In a time where the customer experience separates the winners from the losers, CX must be more of a priority for ALL businesses.

This not only includes the analysis of typical channels in which customers directly interact with your company (calls, chats, emails, feedback, surveys, etc.) but all the channels in which customers may not be interacting directly with you – social, reviews, blogs, comment boards, media, etc.

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In order to understand the purpose of a CX team and how it operates, you first need to understand how most businesses organize, manage, and carry out their customer experiences today.

Essentially, a company’s customer experience is owned and managed by a handful of teams. This includes, but is not limited to:

  • digital,
  • brand,
  • strategy,
  • UX,
  • retail,
  • design,
  • pricing,
  • membership,
  • logistics,
  • marketing,
  • and customer service.

All of these teams have a hand in customer experience.

In order to affirm that they are working towards a common goal, they must

  1. communicate in a timely manner,
  2. meet and discuss upcoming initiatives and projects,
  3. and discuss results along with future objectives.

In a perfect world, every team has the time and passion to accomplish these tasks to ensure the customer experience is in sync with their work. In reality, teams end up scrambling for information and understanding of how each business function is impacting the customer experience – sometimes after the CX program has already launched.

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This process is extremely inefficient and can lead to serious problems across the customer experience. These problems can lead to irreparable financial losses. If business functions are not on the same page when launching an experience, it creates a broken one for customers. Siloed teams create siloed experiences.

There are plenty of companies that operate in a semi-siloed manner and feel it is successful. What these companies don’t understand is that customer experience issues often occur between the ownership of these silos, in what some refer to as the “customer experience abyss,” where no business function claims ownership. Customers react to these broken experiences by communicating their frustration through different communication channels (chats, surveys, reviews, calls, tweets, posts etc.).

For example, if a company launches a new subscription service and customers are confused about the pricing model, is it the job of customer service to explain it to customers?  What about those customers that don’t contact the business at all? Does marketing need to modify their campaigns? Maybe digital needs to edit the nomenclature online… It could be all of these things. The key is determining which will solve the poor customer experience.

The objective of a CX program is to focus deeply on what customers are saying and shift business teams to become advocates for what they say. Once advocacy is achieved, the customer experience can be improved at scale with speed and precision. A premium customer experience is the key to company growth and customer retention. How important is the customer experience?

You may be saying to yourself, “We already have teams examining our customer data, no
need to establish a new team to look at it.” While this may be true, the teams are likely taking a siloed approach to analyzing customer data by only investigating the portion of the data they own.

For example, the social team looks at social data, the digital team analyzes web feedback and analytics, the marketing team reviews surveys and performs studies, etc. Seldom do these teams come together and combine their data to get a holistic view of the customer. Furthermore, when it comes to prioritizing CX improvements, they do so based on an incomplete view of the customer.

Consolidating all customer data gives a unified view of your customers while lessening the workload and increasing the rate at which insights are generated. The experience customers have with marketing, digital, and customer service, all lead to different interactions. Breaking these interactions into different, separate components is the reason companies struggle with understanding the true customer experience and miss the big picture on how to improve it.

The CX team, once established, will be responsible for creating a unified view of the customer which will provide the company with an unbiased understanding of how customers feel about their experiences as well as their expectations of the industry. These insights will provide awareness, knowledge, and curiosity that will empower business functions to improve the end-to-end customer experience.

CX programs are disruptive. A successful CX program will uncover insights that align with current business objectives and some insights that don’t at all. So, what do you do when you run into that stone wall? How do you move forward when a business function refuses to adopt the voice of the customer? Call in back-up from an executive who understands the value of the voice of the customer and why it needs to be top-of mind for every function.

When creating a disruptive program like CX, an executive owner is needed to overcome business hurdles along the way. Ideally, this executive owner will support the program and promote it to the broader business functions. In order to scale and become more widely adopted, it is also helpful to have executive support when the program begins.

The best candidates for initial ownership are typically marketing, analytics or operations executives. Along with understanding the value a CX program can offer, they should also understand the business’ current data landscape and help provide access to these data sets. Once the CX team has access to all the available customer data, it will be able to aggregate all necessary interactions.

Executive sponsors will help dramatically in regard to CX program adoption and eventual scaling. Executive sponsors

  • can provide the funding to secure the initial success,
  • promote the program to ensure other business functions work closer to the program,
  • and remove roadblocks that may otherwise take weeks to get over.

Although an executive sponsor is not necessary, it can make your life exponentially easier while you build, launch, and execute your CX program. Your customers don’t always tell you what you want to hear, and that can be difficult for some business functions to handle. When this is the case, some business functions will try to discredit insights altogether if they don’t align with their goals.

Data grows exponentially every year, faster than any company can manage. In 2016, 90% of the world’s data had been created in the previous two years. 80% of that data was unstructured language. The hype of “Big Data” has passed and the focus is now on “Big Insights” – how to manage all the data and make it useful. A company should not be allocating resources to collecting more data through expensive surveys or market research – instead, they should be focused on doing a better job of listening and reacting to what customers are already saying, by unifying the voice of the customer with data that is already readily available.

It’s critical to identify all the available customer interactions and determine value and richness. Be sure to think about all forms of direct and indirect interactions customers have. This includes:

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These channels are just a handful of the most popular avenues customers use to engage with brands. Your company may have more, less, or none of these. Regardless, the focus should be on aggregating as many as possible to create a holistic view of the customer. This does not mean only aggregating your phone calls and chats; this includes every channel where your customers talk with, at, or about your company. You can’t be selective when it comes to analyzing your customers by channel. All customers are important, and they may have different ways of communicating with you.

Imagine if someone only listened to their significant other in the two rooms where they spend the most time, say the family room and kitchen. They would probably have a good understanding of the overall conversations (similar to a company only reviewing calls, chats, and social). However, ignoring them in the dining room, bedroom, kids’ rooms, and backyard, would inevitably lead to serious communication problems.

It’s true that phone, chat, and social data is extremely rich, accessible, and popular, but that doesn’t mean you should ignore other customers. Every channel is important. Each is used by a different customer, in a different manner, and serves a different purpose, some providing more context than others.

You may find your most important customers aren’t always the loudest and may be interacting with you through an obscure channel you never thought about. You need every customer channel to fully understand their experience.

Click here to access Topbox’s detailed study

Better practices for compliance management

Publié le par hanswillertbleuazurconsultingeu

The main compliance challenges

We know that businesses and government entities alike struggle to manage compliance requirements. Many have put up with challenges for so long—often with limited resources—that they no longer see how problematic the situation has become.

FIVE COMPLIANCE CHALLENGES YOU MIGHT BE DEALING WITH

01 COMPLIANCE SILOS
It’s not uncommon that, over time, separate activities, roles, and teams develop to address different compliance requirements. There’s often a lack of integration and communication among these teams or individuals. The result is duplicated efforts—and the creation of multiple clumsy and inefficient systems. This is then perpetuated as compliance processes change in response to regulations, mergers and acquisitions, or other internal business re-structuring.

02 NO SINGLE VIEW OF COMPLIANCE ASSURANCE
Siloed compliance systems also make it hard for senior management to get an overview of current compliance activities and perform timely risk assessments. If you can’t get a clear view of compliance risks, then chances are good that a damaging risk will slip under the radar, go unaddressed, or simply be ignored.

03 COBBLED TOGETHER, HOME-GROWN SYSTEMS
Using generalized software, like Excel spreadsheets and Word documents, in addition to shared folders and file systems, might have made sense at one point. But, as requirements become more complex, these systems become more frustrating, inefficient, and risky. Compiling hundreds or thousands of spreadsheets to support compliance management and regulatory reporting is a logistical nightmare (not to mention time-consuming). Spreadsheets are also prone to error and limited because they don’t provide audit trails or activity logs.

04 OLD SOFTWARE, NOT DESIGNED TO KEEP UP WITH FREQUENT CHANGES
You could be struggling with older compliance software products that aren’t designed to deal with constant change. These can be increasingly expensive to upgrade, not the most user-friendly, and difficult to maintain.

05 NOT USING AUTOMATED MONITORING
Many compliance teams are losing out by not using analytics and data automation. Instead, they rely heavily on sample testing to determine if compliance controls and processes are working, so huge amounts of activity data is never actually checked.

Transform your compliance management process

Good news! There’s some practical steps you can take to transform compliance processes and systems so that they become way more efficient and far less expensive and painful.

It’s all about optimizing the interactions of people, processes, and technology around regulatory compliance requirements across the entire organization.

It might not sound simple, but it’s what needs to be done. And, in our experience, it can be achieved without becoming massively time-consuming and expensive. Technology for regulatory compliance management has evolved to unite processes and roles across all aspects of compliance throughout your organization.

Look, for example, at how technology like Salesforce (a cloud-based system with big data analytics) has transformed sales, marketing, and customer service. Now, there’s similar technology which brings together different business units around regulatory compliance to improve processes and collaboration for the better.

Where to start?

Let’s look at what’s involved in establishing a technology-driven compliance management process. One that’s driven by data and fully integrated across your organization.

THE BEST PLACE TO START IS THE END

Step 1: Think about the desired end-state.

First, consider the objectives and the most important outcomes of your new process. How will it impact the different stakeholders? Take the time to clearly define the metrics you’ll use to measure your progress and success.

A few desired outcomes:

  • Accurately measure and manage the costs of regulatory and policy compliance.
  • Track how risks are trending over time, by regulation, and by region.
  • Understand, at any point in time, the effectiveness of compliance-related controls.
  • Standardize approaches and systems for managing compliance requirements and risks across the organization.
  • Efficiently integrate reporting on compliance activities with those of other risk management functions.
  • Create a quantified view of the risks faced due to regulatory compliance failures for executive management.
  • Increase confidence and response times around changing and new regulations.
  • Reduce duplication of efforts and maximize overall efficiency.

NOW, WHAT DO YOU NEED TO SUPPORT YOUR OBJECTIVES?

Step 2: Identify the activities and capabilities that will get you the desired outcomes.

Consider the different parts of the compliance management process below. Then identify the steps you’ll need to take or the changes you’ll need to make to your current activity that will help you achieve your objectives. We’ve put together a cheat sheet to help this along.

Galvanize

IDENTIFY & IMPLEMENT COMPLIANCE CONTROL PROCEDURES

  • 01 Maintain a central library of regulatory requirements and internal corporate policies, allocated to owners and managers.
  • 02 Define control processes and procedures that will ensure compliance with regulations and policies.
  • 03 Link control processes to the corresponding regulations and corporate policies.
  • 04 Assess the risk of control weaknesses and failure to comply with regulations and policies.

RUN TRANSACTIONAL MONITORING ANALYTICS

  • 05 Monitor the effectiveness of controls and compliance activities with data analytics.
  • 06 Get up-to-date confirmation of the effectiveness of controls and compliance from owners with automated questionnaires or certification of adherence statements.

MANAGE RESULTS & RESPOND

  • 07 Manage the entire process of exceptions generated from analytic monitoring and from the generation of questionnaires and certifications.

REPORT RESULTS & UPDATE ASSESSMENTS

  • 08 Use the results of monitoring and exception management to produce risk assessments and trends.
  • 09 Identify new and changing regulations as they occur and update repositories and control and compliance procedures.
  • 10 Report on the current status of compliance management activities from high- to low-detail levels.

IMPROVE THE PROCESS

  • 11 Identify duplicate processes and fix procedures to combine and improve controls and compliance tests.
  • 12 Integrate regulatory compliance risk management, monitoring, and reporting with overall risk management activities.

Eight compliance processes in desperate need of technology

01 Centralize regulations & compliance requirements
A major part of regulatory compliance management is staying on top of countless regulations and all their details. A solid content repository includes not only the regulations themselves, but also related data. By centralizing your regulations and compliance requirements, you’ll be able to start classifying them, so you can eventually search regulations and requirements by type, region of applicability, effective dates, and modification dates.

02 Map to risks, policies, & controls
Classifying regulatory requirements is no good on its own. They need to be connected to risk management, control and compliance processes, and system functionality. This is the most critical part of a compliance management system.

Typically, in order to do this mapping, you need:

  • An assessment of non-compliant risks for each requirement.
  • Defined processes for how each requirement is met.
  • Defined controls that make sure the compliance process is effective in reducing non-compliance risks.
  • Controls mapped to specific analytics monitoring tests that confirm the effectiveness on an ongoing basis.
  • Assigned owners for each mapped requirement. Specific processes and controls may be assigned to sub-owners.

03 Connect to data & use advanced analytics

Using different automated tests to access and analyze data is foundational to a data-driven compliance management approach.

The range of data sources and data types needed to perform compliance monitoring can be humongous. When it comes to areas like FCPA or other anti-bribery and corruption regulations, you might need to access entire populations of purchase and payment transactions, general ledger entries, payroll, and travel and entertainment expenses. And that’s just the internal sources. External sources could include things like the Politically Exposed Persons database or Sanctions Checks.

Extensive suites of tests and analyses can be run against the data to determine whether compliance controls are working effectively and if there are any indications of transactions or activities that fail to comply with regulations. The results of these analyses identify specific anomalies and control exceptions, as well as provide statistical data and trend reports that indicate changes in compliance risk levels.

Truly delivering on this step involves using the right technology since the requirements for accessing and analyzing data for compliance are demanding. Generalized analytic software is seldom able to provide more than basic capabilities, which are far removed from the functionality of specialized risk and control monitoring technologies.

04 Monitor incidents & manage issues

It’s important to quickly and efficiently manage instances once they’re flagged. But systems that create huge amounts of “false positives” or “false negatives” can end up wasting a lot of time and resources. On the other hand, a system that fails to detect high risk activities creates risk of major financial and reputational damage. The monitoring technology you choose should let you fine-tune analytics to flag actual risks and compliance failures and minimize false alarms.

The system should also allow for an issues resolution process that’s timely and maintains the integrity of responses. If the people responsible for resolving a flagged issue don’t do it adequately, an automated workflow should escalate the issues to the next level.

Older software can’t meet the huge range of incident monitoring and issues management requirements. Or it can require a lot of effort and expense to modify the procedures when needed.

05 Manage investigations

As exceptions and incidents are identified, some turn into issues that need in-depth investigation. Software helps this investigation process by allowing the user to document and log activities. It should also support easy collaboration of anyone involved in the investigation process.

Effective security must be in place around access to all aspects of a compliance management system. But it’s extra important to have a high level of security and privacy for the investigation management process.

06 Use surveys, questionnaires & certifications

Going beyond just transactional analysis and monitoring, it’s also important to understand what’s actually happening right now, by collecting the input of those working in the front-lines.

Software that has built-in automated surveys and questionnaires can gather large amounts of current information directly from these individuals in different compliance roles, then quickly interpret the responses.

For example, if you’re required to comply with the Sarbanes-Oxley Act (SOX), you can use automated questionnaires and certifications to collect individual sign-off on SOX control effectiveness questions. That information is consolidated and used to support the SOX certification process far more efficiently than using traditional ways of collecting sign-off.

07 Manage regulatory changes

Regulations change constantly, and to remain compliant, you need to know—quickly— when those changes happen. This is because changes can often mean modifications to your established procedures or controls, and that could impact your entire compliance management process.

A good compliance software system is built to withstand these revisions. It allows for easy updates to existing definitions of controls, processes, and monitoring activities.

Before software, any regulatory changes would involve huge amounts of manual activities, causing backlogs and delays. Now much (if not most) of the regulatory change process can be automated, freeing your time to manage your part of the overall compliance program.

08 Ensure regulatory examination & oversight

No one likes going through compliance reviews by regulatory bodies. It’s even worse if failures or weaknesses surface during the examination.

But if that happens to you, it’s good to know that many regulatory authorities have proven to be more accommodating and (dare we say) lenient when your compliance process is strategic, deliberate, and well designed.

There are huge benefits, in terms of efficiency and cost savings, by using a structured and well-managed regulatory compliance system. But the greatest economic benefit happens when you can avoid a potentially major financial penalty as a result of replacing an inherently unreliable and complicated legacy system with one that’s purpose-built and data-driven.

Click here to access Galvanize’s new White Paper

The Role of Trust in Narrowing Protection Gaps

Publié le par hanswillertbleuazurconsultingeu

The Geneva Association 2018 Customer Survey in 7 mature economies reveals that for half of the respondents, increased levels of trust in insurers and intermediaries would encourage additional insurance purchases, a consistent finding across all age groups. In emerging markets this share is expected to be even higher, given a widespread lack of experience with financial institutions, the relatively low presence of well-known and trusted insurer brands and a number of structural legal and regulatory shortcomings.

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Against this backdrop, a comprehensive analysis of the role and nature of trust in insurance, with a focus on the retail segment, is set to offer additional important insights into how to narrow the protection gap—the difference between needed and available protection—through concerted multi-stakeholder efforts.

The analysis is based on economic definitions of trust, viewed as an ’institutional economiser’ that facilitates or even eliminates the need for various procedures of verification and proof, thereby cutting transaction costs.

In the more specific context of insurance, trust can be defined as a customer’s bet on an insurer’s future contingent actions, ranging

  • from paying claims
  • to protecting personal data
  • and ensuring the integrity of algorithms.

Trust is the lifeblood of insurance business, as its carriers sell contingent promises to pay, often at a distant and unspecified point in the future.

From that perspective, we can explore the implications of trust for both insurance demand and supply, i.e. its relevance to the size and nature of protection gaps. For example, trust influences behavioural biases such as customers’ propensity for excessive discounting, or in other words, an irrationally high preference for money today over money tomorrow that dampens demand for insurance. In addition, increased levels of trust lower customers’ sensitivity to the price of coverage.

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Trust also has an important influence on the supply side of insurance. The cost loadings applied by insurers to account for fraud are significant and lead to higher premiums for honest customers. Enhanced insurer trust in their customers’ prospective honesty would enable

  • lower cost loadings,
  • less restrictive product specifications
  • and higher demand for insurance.

The potential for lower cost loadings is significant. In the U.S. alone, according to the Insurance Information Institute (2019), fraud in the property and casualty sector is estimated to cost the insurance industry more than USD 30 billion annually, about 10% of total incurred losses and loss adjustment expenses.

Another area where trust matters greatly to the supply of insurance coverage is asymmetric information. A related challenge is moral hazard, or the probability of a person exercising less care in the presence of insurance cover. In this context, however, digital technologies and modern analytics are emerging as potentially game-changing forces. Some pundits herald the end of the age of asymmetric information and argue that a proliferation of information will

  • counter adverse selection and moral hazard,
  • creating transparency (and trust) for both insurers and insureds
  • and aligning their respective interests.

Other experts caution that this ‘brave new world’ depends on the development of customers’ future privacy preferences.

One concrete example is the technology-enabled rise in peer-to-peer trust and the amplification of word-of-mouth. This general trend is now entering the world of insurance as affinity groups and other communities organise themselves through online platforms. In such business models, trust in incumbent insurance companies is replaced with trust in peer groups and the technology platforms that organise them. Another example is the blockchain. In insurance, some start-ups have pioneered the use of blockchain to improve efficiency, transparency and trust in unemployment, property and casualty, and travel insurance, for example. In more advanced markets, ecosystem partners can serve as another example of technology-enabled trust influencers.

These developments are set to usher in an era in which customer data will be a key source of competitive edge. Therefore, gaining and maintaining customers’ trust in how data is used and handled will be vitally important for insurers’ reputations. This also applies to the integrity and interpretability of artificial intelligence tools, given the potential for biases to be embedded in algorithms.

In spite of numerous trust deficits, insurers appear to be in a promising position to hold their own against technology platforms, which are under increasing scrutiny for dubious data handling practices. According to the Geneva Association 2018 Customer Survey, only 3% of all respondents (and 7% of the millennials) polled name technology platforms as their preferred conduits for buying insurance. Insurers’ future performance, in terms of responsible data handling and usage as well as algorithm building, will determine whether their current competitive edge is sustainable. It should not be taken for granted, as—especially in high-growth markets—the vast majority of insurance customers would at least be open to purchasing insurance from new entrants.

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In order to substantiate a multi-stakeholder road map for narrowing protection gaps through fostering trust, we propose a triangle of determinants of trust in insurance.

  1. First, considering the performance of insurers, how an insurer services a policy and settles claims is core to building or destroying trust.
  2. Second, regarding the performance of intermediaries, it is intuitively plausible that those individuals and organisations at the frontline of the customer interface are critically important to the reputation and the level of trust placed in the insurance carrier.
  3. And third, taking into account sociodemographic factors, most recent research finds that trust in insurance is higher among females.

This research also suggests that trust in insurance decreases with age, and insurance literacy has a strong positive influence on the level of trust in insurance.

Based on this paper’s theoretical and empirical findings, we propose the following road map for ensuring that insurance markets are optimally lubricated with trust. This road map includes 3 stakeholder groups that need to act in concert: insurers (and their intermediaries), customers, and regulators/ lawmakers.

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In order for insurers and their intermediaries to bolster customer trust—and enhance their contribution to society—we recommend they do the following:

  • Streamline claims settlement with processes that differentiate between honest and (potentially) dishonest customers. Delayed claims settlement, which may be attributable to procedures needed for potentially fraudulent customer behaviour, causes people to lose trust in insurers and is unfair to honest customers.
  • Increase product transparency and simplicity, with a focus on price and value. Such efforts could include aligning incentives through technology-enabled customer engagement and utilising data and analytics for simpler and clearer underwriting procedures. This may, however, entail delicate trade-offs between efficiency and privacy.
  • ‘Borrow’ trust: As a novel approach, insurers may partner with non-insurance companies or influencers to access new customers through the implied endorsement of a trusted brand or individual. Such partnerships are also essential to extending the business model of insurance beyond its traditional centre of gravity, which is the payment of claims.

Customers and their organisations are encouraged to undertake the following actions:

  • Support collective action against fraud. Insurance fraud hinders mutual trust and drives cost loadings, which are unfair to honest customers and lead to suboptimal levels of aggregate demand.
  • Engage with insurers who leverage personal data for the benefit of the customer. When insurers respond to adverse selection, they increase rates for everyone in order to cover their losses. This may cause low-risk customers to drop out of the company’s risk pool and forego coverage. ‘Real time’ underwriting methods and modern analytics are potential remedies to the undesirable effects of adverse selection.

Recommendations for policymakers and regulators are the following:

  • Protect customers. Effective customer protection is indispensable to lubricating insurance markets with trust. First, regulators should promote access to insurance through regulations that interfere with the market mechanism for rate determination or through more subtle means, such as restrictions on premium rating factors. Second, regulators should make sure that insurers have the ability to pay claims and remain solvent. This may involve timely prudential regulatory intervention.
  • Promote industry competition. There is a positive correlation between an insurance market’s competitiveness and levels of customer trust. In a competitive market, the cost to customers for switching from an underperforming insurance carrier to a more favourable competitor is relatively low. However, the cost of customer attrition for insurers is high. Therefore, in a competitive market, the onus is on insurers to perform well and satisfy customers.

Click here to access Geneva Association’s Research Debrief

 

Mastering Financial Customer Data at Multinational Scale

Publié le par hanswillertbleuazurconsultingeu

Your Customer Data…Consolidated or Chaotic?

In an ideal world, you know your customers. You know

  • who they are,
  • what business they transact,
  • who they transact with,
  • and their relationships.

You use that information to

  • calculate risk,
  • prevent fraud,
  • uncover new business opportunities,
  • and comply with regulatory requirements.

The problem at most financial institutions is that customer data environments are highly chaotic. Customer data is stored in numerous systems across the company. Most, if not all of which, has evolved over time in siloed environments according to business function. Each system has its

  • own management team,
  • technology platform,
  • data models,
  • quality issues,
  • and access policies.

Tamr1

This chaos prevents the firms from fully achieving and maintaining a consolidated view of customers and their activity.

The Cost of Chaos

A chaotic customer data environment can be an expensive problem in a financial institution. Customer changes have to be implemented in multiple systems, with a high likelihood of error or inconsistency because of manual processes. Discrepancies with the data leads to inevitable remediation activities that are widespread, and costly.

Analyzing customer data within one global bank required three months to compile and validate its correctness. The chaos leads to either

  1. prohibitively high time and cost of data preparation or
  2. garbage-in, garbage-out analytics.

The result of customer data chaos is an incredibly high risk profile — operational, regulatory, and reputational.

Eliminating the Chaos 1.0

Many financial services companies attempt to eliminate this chaos and consolidate their customer data.

A common approach is to implement a master data management (MDM) system. Customer data from different source systems is centralized into one place where it can be harmonized. The output is a “golden record,” or master customer record.

A lambda architecture permits data to stream into the centralized store and be processed in realtime so that it is immediately mastered and ready for use. Batch processes run on the centralized store to perform periodic (daily, monthly, quarterly, etc.) calculations on the data.

First-generation MDM systems centralize customer data and unify it by writing ETL scripts and matching rules.

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The harmonizing often involves:

  1. Defining a common, master schema in which to store the consolidated data
  2. Writing ETL scripts to transform the data from source formats and schemas into the new common storage format
  3. Defining rule sets to deduplicate, match/cluster, and otherwise cleanse within the central MDM store

There are a number of commercial MDM solutions available that support the deterministic approach outlined above. The initial experience with those MDM systems, integrating the first five or so large systems, is often positive. Scaling MDM to master more and more systems, however, becomes a challenge that grows exponentially, as we’ll explain below.

Rules-based MDM, and the Robustness- Versus-Expandability Trade Off

The rule sets used to harmonize data together are usually driven off of a handful of dependent attributes—name, legal identifiers, location, and so on. Let’s say you use six attributes to stitch together four systems, A and B, and then the same six attributes between A and C, then A and D, B and C, B and D, and C and D. Within that example of 4 systems, you would have twenty four potential attributes that you are aligning. Add a fifth system, it’s 60 attributes; a sixth system, 90 attributes. So the effort to master additional systems grows exponentially. And in most multinational financial institutions, the number of synchronized attributes is not six; it’s commonly 50 to 100.

And maintenance is equally burdensome. There’s no guarantee that your six attributes maintain their validity or veracity over time. If any of these attributes need to be modified, then rules need to be redefined across the systems all over again.

The trade off for many financial institutions is robustness versus expandability. In other words, you can have a large-scale data mastering implementation and have it wildly complex, or you can do something small and have it highly accurate.

This is problematic for most financial institutions, which have very large-scale customer data challenges.

Customer Data Mastering at Scale

In larger financial services companies, especially multinationals, the number of systems in which customer data resides is much larger than the examples above. It is not uncommon to see financial companies with over 100 large systems.

Among those are systems that have been:

  • Duplicated in many countries to comply with data sovereignty regulations
  • Acquired via inorganic growth, purchased companies bringing in their own infrastructure for trading, CRM, HR, and back office. Integrating these can take a significant amount of time and cost

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When attempting to master a hundred sources containing petabytes of data, all of which have data linking and matching in different ways across a multitude of attributes and systems, you can see that the matching rules required to harmonize your data together gets incredibly complex.

Every incremental source added to the MDM environment can take thousands of rules to be implemented. Within just a mere handful of systems, the complexity gets to a point where it’s unattainable. As that complexity goes up, the cost of maintaining a rules-based approach also scales wildly, requiring more and more data stewards to make sure all the stitching rules remain correct.

Mastering data at scale is one of the riskiest endeavors a business can take. Gartner reports that 85% of MDM projects fail. And MDM budgets of $10M to $20M per year are not uncommon in large multinationals. With such high stakes, making sure that you get the right approach is critical to making sure that this thing is a success.

A New Take on an Old Paradigm

What follows is a reference architecture. The approach daisy chains together three large tool sets, each with appropriate access policies enforced, that are responsible for three separate steps in the mastering process:

  1. Raw Data Zone
  2. Common Data Zone
  3. Mastered Data Zone

tamr4

Raw Data Zone The first sits on a traditional data lake model—a landing area for raw data. Data is replicated from source systems to the centralized data repository (often built on Hadoop). Data is replicated in real time (perhaps via Kafka) wherever possible so that data is most up to date. For source systems that do not support real-time replication, nightly batch jobs or flat-file ingestion are used.

Common Data Zone Within the Common Data Zone, we take all of the data from the Raw Zone—with the various different objects, in different shapes and sizes, and conform that into outputs that look and feel the same to the system, with the same column headers, data types, and formats.

The toolset in this zone utilizes machine learning models to categorize data that exists within the Raw Data Zone. Machine learning models are trained on what certain attributes look like—what’s a legal entity, or a registered address, or country of incorporation, or legal hierarchy, or any other field. It does so without requiring anyone having to go back to the source system owners to bog them down with questions about that, saving weeks of effort.

This solution builds up a taxonomy and schema for the conformed data as raw data is processed. Unlike early-generation MDM solutions, this substantially reduces data unification time, often by months per source system, because there is:

  • No need to pre-define a schema to hold conformed data
  • No need to write ETL to transform the raw data

One multinational bank implementing this reference architecture reported being able to conform the raw data from a 10,000-table system within three days, and without using up source systems experts’ time defining a schema or writing ETL code. In terms of figuring out where relevant data is located in the vast wilderness this solution is very productive and predictable.

Mastered Data Zone In the third zone, the conformed data is mastered, and the outputs of the mastering process are clusters of records that refer to the same real-world entity. Within each cluster, a single, unified golden, master record of the entity is configured. The golden customer record is then distributed to wherever it’s needed:

  • Data warehouses
  • Regulatory (KYC, AML) compliance systems
  • Fraud and corruption monitoring
  • And back to operational systems, to keep data changes clean at the source

As with the Common Zone, machine learning models are used. These models eliminate the need to define hundreds of rules to match and deduplicate data. Tamr’s solution applies a probabilistic model that uses statistical analysis and naive Bayesian modeling to learn from existing relationships between various attributes, and then makes record-matching predictions based on these attribute relationships.

Tamr matching models require training, which usually takes just a few days per source system. Tamr presents a data steward with its predictions, and the steward can either confirm or deny them to help Tamr perfect its matching.

With the probabilistic model, Tamr looks at all of the attributes on which it has been trained, and based on the attribute matching, the solution will indicate a confidence level of a match being accurate. Depending on a configurable confidence level threshold, It will disregard entries that fall below the threshold from further analysis and training.

As you train Tamr and correct it, it becomes more accurate over time. The more data you throw at te solution, the better it gets. Which is a stark contrast to the rules-based MDM approach, where the more data you throw at it, it tends to break because the rules can’t keep up with the level of complexity.

Distribution A messaging bus (e.g., Apache Kafka) is often used to distribute mastered customer data throughout the organization. If a source system wants to pick up the master copy from the platform, it subscribes to that topic on the messaging bus to receive the feed of changes.

Another approach is to pipeline deltas from the MDM platform into target system in batch.

Real-world Results

This data mastering architecture is in production at a number of large financial institutions. Compared with traditional MDM approaches, the model-driven approach provides the following advantages:

70% fewer IT resources required:

  • Humans in the entity resolution loop are much more productive, focused on a relatively small percentage (~5%) of exceptions that the machine learning algorithms cannot resolve
  • Eliminates ETL and matching rules development
  • Reduces manual data synchronization and remediation of customer data across systems

Faster customer data unification:

  • A global retail bank mastered 35 large IT systems within 6 months—about 4 days per source system
  • New data is mastered within 24 hours of landing in the Raw Data Zone
  • A platform for mastering any category of data—customer, product, suppler, and others

Faster, more complete achievement of data-driven business initiatives:

  • KYC, AML, fraud detection, risk analysis, and others.

 

Click here to access Tamr’s detailed analysistamr4

Building your data and analytics strategy

Publié le par hanswillertbleuazurconsultingeu

When it comes to being data-driven, organizations run the gamut with maturity levels. Most believe that data and analytics provide insights. But only one-third of respondents to a TDWI survey said they were truly data-driven, meaning they analyze data to drive decisions and actions.

Successful data-driven businesses foster a collaborative, goal-oriented culture. Leaders believe in data and are governance-oriented. The technology side of the business ensures sound data quality and puts analytics into operation. The data management strategy spans the full analytics life cycle. Data is accessible and usable by multiple people – data engineers and data scientists, business analysts and less-technical business users.

TDWI analyst Fern Halper conducted research of analytics and data professionals across industries and identified the following five best practices for becoming a data-driven organization.

1. Build relationships to support collaboration

If IT and business teams don’t collaborate, the organization can’t operate in a data-driven way – so eliminating barriers between groups is crucial. Achieving this can improve market performance and innovation; but collaboration is challenging. Business decision makers often don’t think IT understands the importance of fast results, and conversely, IT doesn’t think the business understands data management priorities. Office politics come into play.

But having clearly defined roles and responsibilities with shared goals across departments encourages teamwork. These roles should include: IT/architecture, business and others who manage various tasks on the business and IT sides (from business sponsors to DevOps).

2. Make data accessible and trustworthy

Making data accessible – and ensuring its quality – are key to breaking down barriers and becoming data-driven. Whether it’s a data engineer assembling and transforming data for analysis or a data scientist building a model, everyone benefits from trustworthy data that’s unified and built around a common vocabulary.

As organizations analyze new forms of data – text, sensor, image and streaming – they’ll need to do so across multiple platforms like data warehouses, Hadoop, streaming platforms and data lakes. Such systems may reside on-site or in the cloud. TDWI recommends several best practices to help:

  • Establish a data integration and pipeline environment with tools that provide federated access and join data across sources. It helps to have point-and-click interfaces for building workflows, and tools that support ETL, ELT and advanced specifications like conditional logic or parallel jobs.
  • Manage, reuse and govern metadata – that is, the data about your data. This includes size, author, database column structure, security and more.
  • Provide reusable data quality tools with built-in analytics capabilities that can profile data for accuracy, completeness and ambiguity.

3. Provide tools to help the business work with data

From marketing and finance to operations and HR, business teams need self-service tools to speed and simplify data preparation and analytics tasks. Such tools may include built-in, advanced techniques like machine learning, and many work across the analytics life cycle – from data collection and profiling to monitoring analytical models in production.

These “smart” tools feature three capabilities:

  • Automation helps during model building and model management processes. Data preparation tools often use machine learning and natural language processing to understand semantics and accelerate data matching.
  • Reusability pulls from what has already been created for data management and analytics. For example, a source-to-target data pipeline workflow can be saved and embedded into an analytics workflow to create a predictive model.
  • Explainability helps business users understand the output when, for example, they’ve built a predictive model using an automated tool. Tools that explain what they’ve done are ideal for a data-driven company.

4. Consider a cohesive platform that supports collaboration and analytics

As organizations mature analytically, it’s important for their platform to support multiple roles in a common interface with a unified data infrastructure. This strengthens collaboration and makes it easier for people to do their jobs.

For example, a business analyst can use a discussion space to collaborate with a data scientist while building a predictive model, and during testing. The data scientist can use a notebook environment to test and validate the model as it’s versioned and metadata is captured. The data scientist can then notify the DevOps team when the model is ready for production – and they can use the platform’s tools to continually monitor the model.

5. Use modern governance technologies and practices

Governance – that is, rules and policies that prescribe how organizations protect and manage their data and analytics – is critical in learning to trust data and become data-driven. But TDWI research indicates that one-third of organizations don’t govern their data at all. Instead, many focus on security and privacy rules. Their research also indicates that fewer than 20 percent of organizations do any type of analytics governance, which includes vetting and monitoring models in production.

Decisions based on poor data – or models that have degraded – can have a negative effect on the business. As more people across an organization access data and build  models, and as new types of data and technologies emerge (big data, cloud, stream mining), data governance practices need to evolve. TDWI recommends three features of governance software that can strengthen your data and analytics governance:

  • Data catalogs, glossaries and dictionaries. These tools often include sophisticated tagging and automated procedures for building and keeping catalogs up to date – as well as discovering metadata from existing data sets.
  • Data lineage. Data lineage combined with metadata helps organizations understand where data originated and track how it was changed and transformed.
  • Model management. Ongoing model tracking is crucial for analytics governance. Many tools automate model monitoring, schedule updates to keep models current and send alerts when a model is degrading.

In the future, organizations may move beyond traditional governance council models to new approaches like agile governance, embedded governance or crowdsourced governance.

But involving both IT and business stakeholders in the decision-making process – including data owners, data stewards and others – will always be key to robust governance at data-driven organizations.

SAS1

There’s no single blueprint for beginning a data analytics project – never mind ensuring a successful one.

However, the following questions help individuals and organizations frame their data analytics projects in instructive ways. Put differently, think of these questions as more of a guide than a comprehensive how-to list.

1. Is this your organization’s first attempt at a data analytics project?

When it comes to data analytics projects, culture matters. Consider Netflix, Google and Amazon. All things being equal, organizations like these have successfully completed data analytics projects. Even better, they have built analytics into their cultures and become data-driven businesses.

As a result, they will do better than neophytes. Fortunately, first-timers are not destined for failure. They should just temper their expectations.

2. What business problem do you think you’re trying to solve?

This might seem obvious, but plenty of folks fail to ask it before jumping in. Note here how I qualified the first question with “do you think.” Sometimes the root cause of a problem isn’t what we believe it to be; in other words, it’s often not what we at first think.

In any case, you don’t need to solve the entire problem all at once by trying to boil the ocean. In fact, you shouldn’t take this approach. Project methodologies (like agile) allow organizations to take an iterative approach and embrace the power of small batches.

3. What types and sources of data are available to you?

Most if not all organizations store vast amounts of enterprise data. Looking at internal databases and data sources makes sense. Don’t make the mistake of believing, though, that the discussion ends there.

External data sources in the form of open data sets (such as data.gov) continue to proliferate. There are easy methods for retrieving data from the web and getting it back in a usable format – scraping, for example. This tactic can work well in academic environments, but scraping could be a sign of data immaturity for businesses. It’s always best to get your hands on the original data source when possible.

Caveat: Just because the organization stores it doesn’t mean you’ll be able to easily access it. Pernicious internal politics stifle many an analytics endeavor.

4. What types and sources of data are you allowed to use?

With all the hubbub over privacy and security these days, foolish is the soul who fails to ask this question. As some retail executives have learned in recent years, a company can abide by the law completely and still make people feel decidedly icky about the privacy of their purchases. Or, consider a health care organization – it may not technically violate the Health Insurance Portability and Accountability Act of 1996 (HIPAA), yet it could still raise privacy concerns.

Another example is the GDPR. Adhering to this regulation means that organizations won’t necessarily be able to use personal data they previously could use – at least not in the same way.

5. What is the quality of your organization’s data?

Common mistakes here include assuming your data is complete, accurate and unique (read: nonduplicate). During my consulting career, I could count on one hand the number of times a client handed me a “perfect” data set. While it’s important to cleanse your data, you don’t need pristine data just to get started. As Voltaire said, “Perfect is the enemy of good.”

6. What tools are available to extract, clean, analyze and present the data?

This isn’t the 1990s, so please don’t tell me that your analytic efforts are limited to spreadsheets. Sure, Microsoft Excel works with structured data – if the data set isn’t all that big. Make no mistake, though: Everyone’s favorite spreadsheet program suffers from plenty of limitations, in areas like:

  • Handling semistructured and unstructured data.
  • Tracking changes/version control.
  • Dealing with size restrictions.
  • Ensuring governance.
  • Providing security.

For now, suffice it to say that if you’re trying to analyze large, complex data sets, there are many tools well worth exploring. The same holds true for visualization. Never before have we seen such an array of powerful, affordable and user-friendly tools designed to present data in interesting ways.

Caveat 1: While software vendors often ape each other’s features, don’t assume that each application can do everything that the others can.

Caveat 2: With open source software, remember that “free” software could be compared to a “free” puppy. To be direct: Even with open source software, expect to spend some time and effort on training and education.

7. Do your employees possess the right skills to work on the data analytics project?

The database administrator may well be a whiz at SQL. That doesn’t mean, though, that she can easily analyze gigabytes of unstructured data. Many of my students need to learn new programs over the course of the semester, and the same holds true for employees. In fact, organizations often find that they need to:

  • Provide training for existing employees.
  • Hire new employees.
  • Contract consultants.
  • Post the project on sites such as Kaggle.
  • All of the above.

Don’t assume that your employees can pick up new applications and frameworks 15 minutes at a time every other week. They can’t.

8. What will be done with the results of your analysis?

A company routinely spent millions of dollars recruiting MBAs at Ivy League schools only to see them leave within two years. Rutgers MBAs, for their part, stayed much longer and performed much better.

Despite my findings, the company continued to press on. It refused to stop going to Harvard, Cornell, etc. because of vanity. In his own words, the head of recruiting just “liked” going to these schools, data be damned.

Food for thought: What will an individual, group, department or organization do with keen new insights from your data analytics projects? Will the result be real action? Or will a report just sit in someone’s inbox?

9. What types of resistance can you expect?

You might think that people always and willingly embrace the results of data-oriented analysis. And you’d be spectacularly wrong.

Case in point: Major League Baseball (MLB) umpires get close ball and strike calls wrong more often than you’d think. Why wouldn’t they want to improve their performance when presented with objective data? It turns out that many don’t. In some cases, human nature makes people want to reject data and analytics that contrast with their world views. Years ago, before the subscription model became wildly popular, some Blockbuster executives didn’t want to believe that more convenient ways to watch movies existed.

Caveat: Ignore the power of internal resistance at your own peril.

10. What are the costs of inaction?

Sure, this is a high-level query and the answers depend on myriad factors.

For instance, a pharma company with years of patent protection will respond differently than a startup with a novel idea and competitors nipping at its heels. Interesting subquestions here include:

  • Do the data analytics projects merely confirm what we already know?
  • Do the numbers show anything conclusive?
  • Could we be capturing false positives and false negatives?

Think about these questions before undertaking data analytics projects Don’t take the queries above as gospel. By and large, though, experience proves that asking these questions frames the problem well and sets the organization up for success – or at least minimizes the chance of a disaster.

SAS2

Most organizations understand the importance of data governance in concept. But they may not realize all the multifaceted, positive impacts of applying good governance practices to data across the organization. For example, ensuring that your sales and marketing analytics relies on measurably trustworthy customer data can lead to increased revenue and shorter sales cycles. And having a solid governance program to ensure your enterprise data meets regulatory requirements could help you avoid penalties.

Companies that start data governance programs are motivated by a variety of factors, internal and external. Regardless of the reasons, two common themes underlie most data governance activities: the desire for high-quality customer information, and the need to adhere to requirements for protecting and securing that data.

What’s the best way to ensure you have accurate customer data that meets stringent requirements for privacy and security?

For obvious reasons, companies exert significant effort using tools and third-party data sets to enforce the consistency and accuracy of customer data. But there will always be situations in which the managed data set cannot be adequately synchronized and made consistent with “real-world” data. Even strictly defined and enforced internal data policies can’t prevent inaccuracies from creeping into the environment.

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Why you should move beyond a conventional approach to data governance?

When it comes to customer data, the most accurate sources for validation are the customers themselves! In essence, every customer owns his or her information, and is the most reliable authority for ensuring its quality, consistency and currency. So why not develop policies and methods that empower the actual owners to be accountable for their data?

Doing this means extending the concept of data governance to the customers and defining data policies that engage them to take an active role in overseeing their own data quality. The starting point for this process fits within the data governance framework – define the policies for customer data validation.

A good template for formulating those policies can be adapted from existing regulations regarding data protection. This approach will assure customers that your organization is serious about protecting their data’s security and integrity, and it will encourage them to actively participate in that effort.

Examples of customer data engagement policies

  • Data protection defines the levels of protection the organization will use to protect the customer’s data, as well as what responsibilities the organization will assume in the event of a breach. The protection will be enforced in relation to the customer’s selected preferences (which presumes that customers have reviewed and approved their profiles).
  • Data access control and security define the protocols used to control access to customer data and the criteria for authenticating users and authorizing them for particular uses.
  • Data use describes the ways the organization will use customer data.
  • Customer opt-in describes the customers’ options for setting up the ways the organization can use their data.
  • Customer data review asserts that customers have the right to review their data profiles and to verify the integrity, consistency and currency of their data. The policy also specifies the time frame in which customers are expected to do this.
  • Customer data update describes how customers can alert the organization to changes in their data profiles. It allows customers to ensure their data’s validity, integrity, consistency and currency.
  • Right-to-use defines the organization’s right to use the data as described in the data use policy (and based on the customer’s selected profile options). This policy may also set a time frame associated with the right-to-use based on the elapsed time since the customer’s last date of profile verification.

The goal of such policies is to establish an agreement between the customer and the organization that basically says the organization will protect the customer’s data and only use it in ways the customer has authorized – in return for the customer ensuring the data’s accuracy and specifying preferences for its use. This model empowers customers to take ownership of their data profile and assume responsibility for its quality.

Clearly articulating each party’s responsibilities for data stewardship benefits both the organization and the customer by ensuring that customer data is high-quality and properly maintained. Better yet, recognize that the value goes beyond improved revenues or better compliance.

Empowering customers to take control and ownership of their data just might be enough to motivate self-validation.

Click her to access SAS’ detailed analysis