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Notes from 2017 MIT CDOIQ Symposium: CDO’s, and Data, go Mainstream

During the conclusion of this year’s annual MIT Chief Data Officer and Information Quality Symposium (MITCDOIQ) held July 12-14 in Cambridge, MA, the organizers noted that the focus of the event was at last shifting from the rise and role of the CDO to a focus on the key, strategic business initiatives undertaken by the CDO and their organization.  But this shift was evident right from the start of the event – one featured session called this the process of “Shifting from Survival to Success.”

Front and center were key topics around the business value of data:  building out the data infrastructure and democratizing data, the data privacy requirements of GDPR, the value and use of machine learning and advanced analytics to drive business initiatives and achieve better business outcomes, and adaption to the accelerating pace of change.  One panel of CDO’s – including Christina Clark of General Electric (GE), Mark Ramsey of Glaxo Smith Kline, and Venkat Varadachary of American Express – addressed a number of these points in detail.

blog MITCDOIQ presentation Notes from 2017 MIT CDOIQ Symposium: CDO’s, and Data, go Mainstream

Managing Data for Business Value and Growth

Though the CDO role emerged out of the financial crisis and the need for broader controls around regulatory requirements and data governance, that role has changed and most new CDO’s are addressing the need for business value and revenue growth.  This doesn’t mean that the foundation and fundamentals of data governance and compliance are forgotten or dropped.  In fact, those aspects are critical to this shift as business growth is dependent on an organization’s ability to structure and manage data for speed and flexibility.  The approaches taken to succeed may vary considerably though.

blog banner landscape Notes from 2017 MIT CDOIQ Symposium: CDO’s, and Data, go Mainstream

One participant noted the example of Google absorbing YouTube.  Google took 2 years to build the infrastructure and information needed to effectively monetize the volumes of data acquired.  In the case of financial services firms, that approach is rare, particularly with mergers.  In those cases, the customer support and client relationship aspects are critical and it is more important to leave the systems in place and develop an approach to span across those systems, even where they have similar data.

The participants particularly noted that the benefits of this data-driven business approach are large, but that there is a need for a clear vision of what the organization wants to do and address.  This includes establishing a scorecard with quantifiable metrics for business value.

Some of these may be as straightforward as: identifying how many different revenue-generating use cases have been deployed; or determining how much faster deployment is with new approaches to data delivery.  Often, it’s the critical data elements, perhaps no more than 50 per subject area, that provide the key metrics around operational value (and the impact to business processes that will break if incorrect) tied in with the costs to acquire, manage, and consume such data.

blog MITCDOIQ trillium Notes from 2017 MIT CDOIQ Symposium: CDO’s, and Data, go Mainstream

I was on hand with colleagues at MITCDOIQ to demonstrate how our Trillium data quality software can help organization’s data governance initiatives 

The CDO of Glaxo Smith Kline noted a goal to change the time to discover new drugs from 8 years to 1 year, and transform the pharmaceutical industry by leveraging sensor-based and genomics data.  Many steps are needed to reach that business goal including standardizing internal data and being able to connect the internal data to new external sources.

Democratization of Data

For individuals in an organization to be effective, they need trusted data at hand to move forward with speed and efficiency.  Data scientists are just a part of that equation.  Sales, marketing, operations, and others in the lines of business all need data.  Getting data into the hands of employees, even if imperfect, is valuable – it creates incentives sooner as people can see the data issues and work to solve them.  Helping solve the problems for these people in accessing and using data not only democratizes the data, but provides them the ability to act in a more agile way with faster time-to-value.

At the same time, it is important to remember that data must be served in a manner that is consumable to these varied users.  Some will want visualizations and dashboards, some need alerts and notifications for faster action, some need data in Excel, and some could care less about visualization and want access via tools such as Python.

To achieve this democratization, it’s important then to understand what data people want access to, how it may be delivered and consumed, and how individuals can accelerate this process.  Shifting the cultural mindset to a process of collecting and accessing data rather than modeling and structuring the data first helps to more readily identify where the business challenges are and how data may be applied to solve the issues and drive value.

Barbara Latulippe, CDO of Dell, reiterated many of the panels themes in her MITCDOIQ presentation on “Governance and Stewardship in the Big Data Era.”  She noted that the data scientists in her organization were struggling to find data.  In one case, it took 35 phone calls by a data scientist to determine all the context around the data!  Democratizing data means it is critical to make the data easy to find, easy to understand, and easy to determine trust and quality.

One metric for Dell is simply reducing the time needed to find and consume data for prescriptive value with a goal to move from 70% of a data scientist’s available time spent in finding data (a statistic regularly reported) to 30% of their time.  Achieving this requires data governance, echoing the earlier panel’s comments that governance is foundational to success in this area.  Dell’s approach follows a Lean Data Governance model, a practice that Trillium Software has noted in the past, including:  starting small, showing success, visualizing results, and breaking down silos by showing others “what’s in it for them.”

Finding Data Skills, Building Data Literacy

On the final day of MITCDOIQ, Natalie Evans Harris, VP of Ecosystem Development at The Impact Lab, discussed the perceived issue in finding individuals with the data skills needed to help organizations achieve business value and growth.  She noted that this is often a “signaling” problem.

The focus by organizations on finding the “data scientist” who can understand and communicate with the business while finding and accessing data, testing hypotheses, building algorithms and models, and ramping these up into ongoing executable frameworks is misguided.  What organizations need to focus on is bringing teams together with the mix of skills that can empower all involved to move the organization forward.  This is the approach noted by Booz, Allen, Hamilton in their Field Guide to Data Science.

It’s important to remember that the range of skills needed to work effectively with data exist in many individuals and consider whether we are really looking for specialists or trying to take advantage of competencies (e.g. biologists, linguists, etc. can provide data science) and blend those with the subject matter experts who understand the business, understand business opportunities, and can present ideas in a manner that makes sense in the organization.

My own topic at MITCDOIQ, Finding Relevance in the Big Data World, touched on an aspect of data literacy, specifically how to approach the challenge of considering what data is important, i.e. relevant, for a given business initiative. Wolf Ruzicka, the Chairman of the Board at EastBanc Technologies, noted in his blog “Grow A Data Tree Out Of The “Big Data” Swamp” of June 1, 2017. “If you don’t know what you want to get out of the data, how can you know what data you need – and what insight you’re looking for?”

A fundamental step then in bringing data into the mainstream is ensuring that the individuals working with the data to establish a goal (whether generating new revenue, meeting compliance goals such as GDPR, or reducing operational costs).  Only with a business goal in mind can you test hypotheses, evaluate and measure data, and determine whether the data is fit for purpose.  The results must be documented in a way that they can be communicated out through a repeatable data governance process.  Such a process should start small, but it provides an approach to build a practice, show success, and build business value while democratizing and measuring the data used and highlighting which data has value for which business purpose.

As Harris noted, it’s important to address change management services and processes, particularly to understand how people can use, interpret, and understand their data and their dashboards.  This means not only thinking about data literacy, but building data literacy!

lessons learned Notes from 2017 MIT CDOIQ Symposium: CDO’s, and Data, go Mainstream

From MITCDOIQ: Lessons Learned in Dell’s CDO/Data Governance Journey

Data-driven Success

As the CDO panel noted, having both data governance and data science teams together in the organization helps ensure that regulatory obligations are met while building for growth.  It’s the underlying foundation needed to achieve success at data-driven initiatives.  And it’s hard to get people bought in fully, and requires culture change, but that is part of the CDO’s work.  This shift is evidenced in even at MITCDOIQ in its topics – no longer is the focus on creating a CDO office but on sharing the stories of organizational change and the adoption of fundamental data-driven processes and data literacy.

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CDOs say data accessibility plans should be theirs to lead

The question of what data to liberate for use in self-service analytics applications and what data to lock down continues to vex many businesses. Most organizations today would like to consider themselves data-driven, and at the heart of that posture is often a self-service ecosystem that gives large numbers of users access to data and the ability to analyze it. At the same time, large-scale data breaches continue to dominate headlines, highlighting the risks of open access.

For Nicholas Marko, chief data officer at Geisinger Health System in Danville, Pa., it’s up to CDOs like him to figure out how to strike the balance between data accessibility and security. Speaking at the 2015 MIT Chief Data Officer & Information Quality Symposium in Cambridge, Mass., last week, Marko said responsibility for an organization’s data strategy isn’t really a good fit anywhere else. It requires more strategic thinking than IT departments typically are used to and is less focused on the traditional domain of CIOs, the selection of new hardware and software, he added. But he sees it as a good match for the chief data officer (CDO), whose job descriptions are still being written in many organizations, but generally include responsibilities related to the strategic use of data.

Finding the proper balance between accessibility and security is crucial to the success of business intelligence efforts. The easiest way to protect data is to lock it away behind a firewall, but the more layers of security you add, the more difficult it is for users to access information. That can hamper data sharing and self-service BI and analytics projects.

Working in healthcare, Marko said he’s seen the pendulum swing too far in the direction of locking down data. This is partly due to particularities of the industry, which is governed by the federal Health Insurance Portability and Accountability Act, a law that specifies strict patient privacy requirements. In many cases, “the problem isn’t securing data,” he said. “Sometimes the problem is un-securing data.”

Breaches breed more caution on risks

Not every industry faces the same kind of regulatory stick when it comes to protecting data. But with the large number of high-profile data breaches in the past few years, more and more businesses in less regulated industries are also seeking to minimize their risks. Even without the threat of regulatory punishment, there’s still the risk of reputational harm — as well as possible financial losses and legal liabilities — that can come from a breach.

All data is not created equal. Mark RamseyCDO at GlaxoSmithKline

That’s not to say balancing data accessibility and security is a glamorous task. Figuring out which data is sensitive and needs tight protections, and identifying employee roles that should be granted access to data can be political and time-consuming. Business departments often control their own systems and don’t want anyone telling them they’re going to have limited access to the data in those systems.

Derek Strauss, CDO at online brokerage TD Ameritrade Inc., said during a session at the conference that when he first took on his current role four years ago, he didn’t want to go anywhere near the issue of data accessibility because it was so political. But, he added that he came to see it as a central function of his role. No one in the organization is better positioned to bring together heads of different departments and help them come to a consensus on accessibility versus security, Strauss said. “The CDO has to step into that role and orchestrate the solutions.”

Some separation of data is natural

The biggest thing a CDO can do to support a healthy balance between access and security is to partition data logically through classifications and privilege settings, conference speakers advised. Marko said that identifying and classifying data according to metadata tags can be helpful. Mark Ramsey, CDO at U.K.-based pharmaceutical maker GlaxoSmithKline PLC, said setting access privileges based on report type is also a good way to maintain access control through partitioning.

For example, Ramsey said that reports about a company’s financial statements are highly sensitive and shouldn’t be shared widely throughout the organization. On the other hand, location-based marketing data is usually rather general and, therefore, not all that sensitive — as a result, there’s less at stake when it is accessed by users. “All data is not created equal,” he said.

Not everyone agrees that balancing data accessibility against security should be within the purview of the CDO. Eugene Kolker, CDO at Seattle Children’s Hospital, said during the same panel discussion in which Marko took part that his organization and many others already have a chief information security officer. In his view, the CDO should be more focused on making sure employees understand the data they have access to and are knowledgeable about the tools they have at their disposal. It would effectively be doubling up on that person’s efforts for CDOs to engage so heavily in the realm of data security, Kolker noted. “The CDO can’t do everything,” he said.

Ed Burns is site editor of SearchBusinessAnalytics. Email him at eburns@techtarget.com and follow him on Twitter: @EdBurnsTT.

Next Steps

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