2018 AI predictions – 8 insights to shape your business strategy

  1. AI will impact employers before it impacts employment
  2. AI will come down to earth—and get to work
  3. AI will help answer the big question about data
  4. Functional specialists, not techies, will decide the AI talent race
  5. Cyberattacks will be more powerful because of AI—but so
    will cyberdefense
  6. Opening AI’s black box will become a priority
  7. Nations will spar over AI
  8. Pressure for responsible AI won’t be on tech companies alone

Key implications

1) AI will impact employers before it impacts employment

As signs grow this year that the great AI jobs disruption will be a false alarm, people are likely to more readily accept AI in the workplace and society. We may hear less about robots taking our jobs, and more about robots making our jobs (and lives) easier. That in turn may lead to a faster uptake of AI than some organizations are expecting.

2) AI will come down to earth—and get to work

Leaders don’t need to adopt AI for AI’s sake. Instead, when they look for the best solution to a business need, AI will increasingly play a role. Does the organization want to automate billing, general accounting and budgeting, and many compliance functions? How about automating parts of procurement, logistics, and customer care? AI will likely be a part of the solution, whether or not users even perceive it.

3) AI will help answer the big question about data

Those enterprises that have already addressed data governance for one application will have a head start on the next initiative. They’ll be on their way to developing best practices for effectively leveraging their data resources and working across organizational boundaries. There’s no substitute for organizations getting their internal data ready to support AI and other innovations, but there is a supplement: Vendors are increasingly taking public sources of data, organizing it into data lakes, and preparing it for AI to use.

4) Functional specialists, not techies, will decide the AI talent race

Enterprises that intend to take full advantage of AI shouldn’t just bid for the most brilliant computer scientists. If they want to get AI up and running quickly, they should move to provide functional specialists with AI literacy. Larger organizations should prioritize by determining where AI is likely to disrupt operations first and start upskilling there.

5) Cyberattacks will be more powerful because of AI—but so will cyberdefense

In other parts of the enterprise, many organizations may choose to go slow on AI, but in cybersecurity there’s no holding back: Attackers will use AI, so defenders will have to use it too. If an organization’s IT department or cybersecurity provider isn’t already using AI, it has to start thinking immediately about AI’s short- and long-term security applications. Sample use cases include distributed denial of service (DDOS) pattern recognition, prioritization of log alerts for escalation and investigation, and risk-based authentication. Since even AI-wary organizations will have to use AI for cybersecurity, cyberdefense will be many enterprises’ first experience with AI. We see this fostering familiarity with AI and willingness to use it elsewhere. A further spur to AI acceptance will come from its hunger for data: The greater AI’s presence and access to data throughout an organization, the better it can defend against cyberthreats. Some organizations are already building out on-premise and cloud-based “threat lakes,” that will enable AI capabilities.

6) Opening AI’s black box will become a priority

We expect organizations to face growing pressure from end users and regulators to deploy AI that is explainable, transparent, and provable. That may require vendors to share some secrets. It may also require users of deep learning and other advanced AI to deploy new techniques that can explain previously incomprehensible AI. Most AI can be made explainable—but at a cost. As with any other process, if every step must be documented and explained, the process becomes slower and may be more expensive. But opening black boxes will reduce certain risks and help establish stakeholder trust.

7) Nations will spar over AI

If China starts to produce leading AI developments, the West may respond. Whether it’s a “Sputnik moment” or a more gradual realization that they’re losing their lead, policymakers may feel pressure to change regulations and provide funding for AI. More countries should issue AI strategies, with implications for companies. It wouldn’t surprise us to see Europe, which is already moving to protect individuals’ data through its General Data Protection Regulation (GDPR), issue policies to foster AI in the region.

8) Pressure for responsible AI won’t be on tech companies alone

As organizations face pressure to design, build, and deploy AI systems that deserve trust and inspire it, many will establish teams and processes to look for bias in data and models and closely monitor ways malicious actors could “trick” algorithms. Governance boards for AI may also be appropriate for many enterprises.

AI PWC

Click here to access PWC’s detailed predictions report

 

The General Data Protection Regulation (GDPR) Primer – What The Insurance Industry Needs To Know, And How To Overcome Cyber Risk Liability As A Result.

SCOPE

The regulation applies if the

  • data controller (organization that collects data from EU residents)
  • or processor (organization that processes data on behalf of data controller e.g. cloud service providers)
  • or the data subject (person)

is based in the EU. Furthermore, the Regulation also applies to organizations based outside the European Union if they collect or process personal data of EU residents. Per the European Commission, “personal data is any information relating to an individual, whether it relates to his or her private, professional or public life. It can be anything from

  • a name,
  • a home address,
  • a photo,
  • an email address,
  • bank details,
  • posts on social networking websites,
  • medical information,
  • or a computer’s IP address.”

The regulation does not apply to the processing of personal data for national security activities or law enforcement; however, the data protection reform package includes a separate Data Protection Directive for the police and criminal justice sector that provides robust rules on personal data exchanges at national, European and international level.

SINGLE SET OF RULES AND ONE-STOP SHOP

A single set of rules will apply to all EU member states. Each member state will establish an independent Supervisory Authority (SA) to hear and investigate complaints, sanction administrative breaches, etc. SA’s in each member state will cooperate with other SA’s, providing mutual assistance and organizing joint operations. Where a business has multiple establishments in the EU, it will have a single SA as its “lead authority”, based on the location of its “main establishment” (i.e., the place where the main processing activities take place). The lead authority will act as a “one-stop shop” to supervise all the processing activities of that business throughout the EU. A European Data Protection Board (EDPB) will coordinate the SAs.

There are exceptions for data processed in an employment context and data processed security, that still might be subject to individual country regulations.

RESPONSIBILITY AND ACCOUNTABILITY

The notice requirements remain and are expanded. They must include the retention time for personal data and contact information for data controller and data protection officer must be provided.

Automated individual decision-making, including profiling (Article 22) is made disputable. Citizens now have the right to question and fight decisions that affect them that have been made on a purely computer generated basis.

To be able to demonstrate compliance with the GDPR, the data controller should implement measures which meet the principles of data protection by design and data protection by default. Privacy by Design and by Default require that data protection measures are designed into the development of business processes for products and services. Such measures include pseudonymizing personal data, by the controller, as soon as possible.

It is the responsibility and liability of the data controller to implement effective measures and can demonstrate the compliance of processing activities even if the processing is carried out by a data processor on behalf of the controller.

Data Protection Impact Assessments must be conducted when specific risks occur to the rights and freedoms of data subjects. Risk assessment and mitigation is required and prior approval of the Data Protection Authorities (DPA) is required for high risks. Data Protection Officers (DPO) are to ensure compliance within organizations.

DPO must be appointed:

  • for all public authorities, except for courts acting in their judicial capacity
  • if the core activities of the controller or the processor consist of
  • by their nature, their scope and/or their purposes, require regular and systematic
    monitoring of data subjects on a large scale
  • processing on a large scale of special categories of data pursuant to Article 9 and
    personal data relating to criminal convictions and offences referred to in Article 10
    processing operations which, for the purposes of national

GDPR in a Box

 

Click here to access Clarium’s detailed paper

State of Digital Analytics: The Persistent Challenge of Data Access & Governance

Disjointed, inaccessible data is a major productivity inhibitor for analytics teams, diverting skilled resources from contributing to valuable business intelligence.

Analytics teams struggle with data access. In addition to listing data silos and data access among both their top data and analytics challenges, above, nearly three in five said it takes days or weeks to access all the data needed for their work or the work of the teams they manage. Only a third were able to access all their data in a day or less.

AMOUNT OF TIME FOR ANALYSTS AND ANALYTICS TEAMS TO ACCESS DATA

Nearly two in five analytics professionals are spending more than half of their work week on tasks unrelated to actual analysis. Forty-four percent of managers reported that more than half of their team’s work week is spent accessing, blending, and preparing data rather than analyzing it, while 31 percent of analysts said they spend more than half of their work week on data housekeeping.

TIME SPENT PREPPING DATA, RATHER THAN ANALYZING IT

As a result, the majority of analysts have found it necessary to learn programming languages specifically to help them access and/or prepare data for analysis. Outside of mandates from their employers, a full 70 percent of analysts reported taking it upon themselves to learn to code for this reason, and more than a quarter of those analysts have spent 80 or more hours learning to program.

ANALYSTS LEARNING PROGRAMMING SKILLS TO OVERCOME DATA ISSUES

It should go without saying that data professionals tasked with analyzing organizational information meaningfully and actionably cannot adequately perform their core job function without accurate data. Yet in addition to raising the data access challenges above, the industry is also split in terms of confidence in data accuracy. Nearly half reported that they question the accuracy of the data they or the teams they manage use regularly, while a little more than half said they are confident about their data.

Data Analysis

Click here to access TMMData’s detailed Survey Results

Creating a Data-Driven Enterprise with DataOps

Let’s discuss why data is important, and what a data-driven organization is. First and foremost, a data-driven organization is one that understands the importance of data. It possesses a culture of using data to make all business decisions. Note the word all. In a datadriven organization, no one comes to a meeting armed only with hunches or intuition. The person with the superior title or largest salary doesn’t win the discussion. Facts do. Numbers. Quantitative analyses. Stuff backed up by data.

Why become a data-driven company? Because it pays off. The MIT Center for Digital Business asked 330 companies about their data analytics and business decision-making processes. It found that the more companies characterized themselves as data-driven, the betterthey performed on objective measures of financial and operational success. Specifically, companies in the top third of their industries when it came to making data-driven decisions were, on average, five percent more productive and six percent more profitable than their competitors. This performance difference remained even after accounting for labor, capital, purchased services, and traditional IT investments. It was also statistically significant and reflected in increased stock market prices that could be objectively measured.

Another survey, by The Economist Intelligence Unit, showed a clear connection between how a company uses data, and its financial success. Only 11 percent of companies said that their organization makes “substantially” better use of data than their peers. Yet more than a third of this group fell into the category of “top performing companies.” The reverse also indicates the relationship between data and financial success. Of the 17 percent of companies that said they “lagged” their peers in taking advantage of data, not one was a top-performing business.

But how do you become a data-driven company? According to a Harvard Business Review article written by McKinsey executives, being a data-driven company requires simultaneously undertaking three interdependent initiatives:

Identify, combine, and manage multiple sources of data

You might already have all the data you need. Or you might need to be creative to find other sources for it. Either way, you need to eliminate silos of data while constantly seeking out new sources to inform your decision-making. And it’s critical to remember that when mining data for insights, demanding data from different and independent sources leads to much better decisions. Today, both the sources and the amount of data you can collect has increased by orders of magnitude. It’s a connected world, given all the transactions, interactions, and, increasingly, sensors that are generating data. And the fact is, if you combine multiple independent sources, you get better insight. The companies that do this are in much better shape, financially and operationally.

Build advanced analytics models for predicting and optimizing outcomes

The most effective approach is to identify a business opportunity and determine how the model can achieve it. In other words, you don’t start with the data—at least at first—but with a problem.

Transform the organization and culture of the company so that data actually produces better business decisions

Many big data initiatives fail because they aren’t in sync with a company’s day-to-day processes and decision-making habits. Data professionals must understand what decisions their business users make, and give users the tools they need to make those decisions.

DD Enterprise

Click here to access the ebook Data Driven Organizations

A Field Guide to Data Science

  • Data Science is the art of turning data into actions.

It’s all about the tradecraft. Tradecraft is the process, tools and technologies for humans and computers to work together to transform data into insights.

  • Data Science tradecraft creates data products.

Data products provide actionable information without exposing decision makers to the underlying data or analytics (e.g., buy/sell strategies for financial instruments, a set of actions to improve product yield, or steps to improve product marketing).

  • Data Science supports and encourages shifting between deductive (hypothesis-based) and inductive (patternbased) reasoning.

This is a fundamental change from traditional analysis approaches. Inductive reasoning and exploratory data analysis provide a means to form or refine hypotheses and discover new analytic paths. Models of reality no longer need to be static. They are constantly tested, updated and improved until better models are found.

  • Data Science is necessary for companies to stay with the pack and compete in the future.

Organizations are constantly making decisions based on gut instinct, loudest voice and best argument – sometimes they are even informed by real information. The winners and the losers in the emerging data economy are going to be determined by their Data Science teams.

  • Data Science capabilities can be built over time.

Organizations mature through a series of stages – Collect, Describe, Discover, Predict, Advise – as they move from data deluge to full Data Science maturity. At each stage, they can tackle increasingly complex analytic goals with a wider breadth of analytic capabilities. However, organizations need not reach maximum Data Science maturity to achieve success. Significant gains can be found in every stage.

  • Data Science is a different kind of team sport.

Data Science teams need a broad view of the organization. Leaders must be key advocates who meet with stakeholders to ferret out the hardest challenges, locate the data, connect disparate parts of the business, and gain widespread buy-in.

Data Science Activities

2015-field-guide-to-data-science-160211215115