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Do You Need A Data Strategy?

20200608 ThinkingDataStrategy

During my time teaching Data Strategy in the class room, I’m frequently asked the question, “how do I know if I need a data strategy?”  For those of you that are deep thinkers, business strategists, or even data architects, I suspect your answer is either “yes!” or “why not?”.

When I’m asked that question, I actually think there’s a different question at hand, “Should I invest the time in developing a data strategy instead of something else?”

In today’s business world, there’s not a shortage of “to do list” items.  So, prioritizing the development of a Data Strategy means deprioritizing some other item.   In order to understand the relative priority and benefit of a Data Strategy initiative, take a look at the need, pain, or problem you’re addressing along with the quantity of people affected.  Your focus should be understanding how a Data Strategy initiative will benefit the team members’ ability to do their job.

To get started, I usually spend time up front interviewing folks to understand the strengths, weaknesses, challenges, and opportunities that exist with data within a company (or organization).  Let me share 5 questions that I always ask.

  1. Is the number of users (or organizations) building queries/reports to analyze data growing?
  2. Are there multiple reports containing conflicting information?
  3. Can a new staff member find and use data on their own, or does it require weeks or months of staff mentoring?
  4. Is data systematically inspected for accuracy (and corrected)? Is anyone responsible for fixing “broken data”?
  5. Is anyone responsible for data sharing?

While you might think these questions are a bit esoteric, each one has a specific purpose.  I’m a big fan of positioning any new strategy initiative to clearly identify the problems that are going to be solved.  If you’re going to undertake the development of a Data Strategy, you want to make certain that you will improve staff members’ ability to make decisions and be more effective at their jobs.  These questions will help you identify where people struggle getting the job done, or where there’s an unquantified risk with using data to make decisions.

So, let me offer an explanation of each question.

  1. “Is the number of users (or organizations) building queries/reports to analyze data growing”

The value of a strategy is directly proportional to the number of people that are going to be affected. In the instance of a data strategy, it’s valuable to understand the number of people that use data (hands-on) to make decisions or do their jobs. If the number is small or decreasing, a strategy initiative may not be worth the investment in time and effort.  The larger the number, the greater the impact to the effectiveness (and productivity) to the various staff members.

  1. “Are there multiple reports containing conflicting information? “

If you have conflicting details within your company that means decisions are made with inaccurate data.  That also means that there’s mistrust of information and team members are spending time confirming details.  That’s business risk and a tremendous waste of time.

  1. “Can a new staff member find and use data…”

If a new staff member can’t be self-sufficient after a week or two on the job (when it comes to data access and usage), you have a problem.  That’s like someone joining the company and not having access to office supplies, a parking space, and email.  And, if the only way to learn is to beg for time for other team members – your spending time with two people not doing their job. It’s a problem that’s being ignored.

  1. “Is data systematically inspected for accuracy (and corrected)? …”

This item is screaming for attention.  If you’re in a company that uses data to make decisions, and no one is responsible for inspecting the content, you have a problem.  Think about this issue another way:   would you purchase hamburger at the grocery store if there was a sign that stated “Never inspected.  May be spoiled.  Not our responsibility”?

  1. Is anyone responsible for data sharing?

This item gets little attention in most companies and is likely the most important of all the questions.  If data is a necessary ingredient in decision making and there isn’t anyone actively responsible for ensuring that new data assets are captured, stored, tracked, managed, and shared, you’re saying that data isn’t a business asset.  (How many assets in the company aren’t tied to someone’s responsibilities?)

If the answer to all of the questions is “no” – great.  You’re in an environment where data is likely managed in a manner that supports a multitude of team members’ needs across different organizations.  If you answered “yes” to a single question, it’s likely that an incremental investment in a tactical data management effort would be helpful.  If more than 1 question is answered “yes”, your company (and the team) will benefit from a Data Strategy initiative.

 

Data Strategy. Why it Matters

I’ve been consulting in the data management space for quite a few years, and I’m often asked about the importance and need for a Data Strategy.   

All too often, the idea of “strategy” brings the images of piles of papers, academics-styled charts, and a list of unachievable goals identifying the topic at hand, but not reflecting reality.  Developing a strategy isn’t about identifying perfection – it’s about identifying a set of goals that address problems and needs that require attention.   A solid data strategy isn’t about identifying perfection, it’s about identifying a set of goals that are achievable and good enough to improve your data environment.  A data strategy is also about identifying the tasks and activities necessary to achieve those goals.  A data strategy is more than the finish line, it’s about the path of the journey. And, it’s about making sure the journey and goal are possible.

Companies spend a fortune on data.  They purchase servers and storage farms to store the data, database management systems to manage the data, transformation tools to convert and transform the data, data quality tools to fix and standardize the content, and treasure trove of analytical tools to present content that can be understood by business people. Given all of the activities, the players, and the content, why would you not want a plan?

Unfortunately, few organizations have a Data Strategy.  They have lots of technology plans and roadmaps. They have platform and server plans; they have DBMS standards; they have storage strategies; they likely have analytical tool plans. While these are valuable, they are typically focused on an organization or function with minimal concern for all of the related upstream and downstream activities (how usable is a data warehouse if the data exists as multiple copies with different names and different formats, and hasn’t been checked/fixed for accuracy?) A data strategy is a plan that ensures that data is easy to find, easy to identify, easy to use, and easy to share across the company and across multiple functions. 

Information technologists are exceptionally strong in the world of applications, tools, and platforms.  They understand the importance of ensuring “reusability” and the benefit of an “economies-of-scale” approach. These are both just nice sound bites focused on making sure that new development work doesn’t always require reinvention.  Application strategies include identifying standards (tools, platforms, storage locations, etc.) and repeatable methods to ensure efficient construction and delivery of data that can be serviced, maintained, and upgraded. An assembly line of sorts.

The challenge with most data environments is that a data strategy rarely exists; there is no repeatable methods and practices.  Every new request requires building data and the associated deliverables from scratch.  And, once delivered, there’s a huge testing and confirmation effort to ensure that the data is accurate.   If you had a data strategy, you’d have reusable data, repeatable methods, and the details would be referenceable online instead of through tribal knowledge.  And delivery efficiency and cost would improve over time. 

Why do you need a data strategy?  Because the cost of data is growing –and it should be shrinking.  The cost of data processing has shrunk, the cost of data storage has decreased dramatically, but the cost of data delivery continues to grow.  A data strategy focuses on delivering data that is easy to find, easy to use, and easy to share.  

Shadow IT: Déjà Vu All Over Again

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I’m a bit surprised with all of the recent discussion and debate about Shadow IT.  For those of you not familiar with the term, Shadow IT refers to software development and data processing activities that occur within business unit organizations without the blessing of the Central IT organization.  The idea of individual business organizations purchasing technology, hiring staff members, and taking on software development to address specific business priorities isn’t a new concept; it’s been around for 30 years.

When it comes to the introduction of technology to address or improve business process, communications, or decision making, Central IT has traditionally not been the starting point.  It’s almost always been the business organization.  Central IT has never been in the position of reengineering business processes or insisting that business users adopt new technologies; that’s always been the role of business management.  Central IT is in the business of automating defined business processes and reducing technology costs (through the use of standard tools, economies-of-scale methods, commodity technologies).   It’s not as though Shadow IT came into existence to usurp the authority or responsibilities of the IT organization.  Shadow IT came into existence to address new, specialized business needs that the Central IT organization was not responsible for addressing.

Here’s a few examples of information technologies that were introduced and managed by Shadow IT organizations to address specialized departmental needs.

  • Word Processing. Possibly the first “end user system” (Wang, IBM DisplayWrite, etc.) This solution was revolutionary in reducing the cost of  documentation
  • The minicomputer.  This technology revolution of the 70’s and 80’s delivered packaged, departmental application systems (DEC, Data General, Prime, etc.)  The most popular were HR, accounting, and manufacturing applications.
  • The personal computer.  Many companies created PC support teams (in Finance) because they required unique skills that didn’t exist within most companies.
  • Email, File Servers, and Ethernet (remember Banyan, Novell, 3com).  These tools worked outside the mainframe OLTP environment and required specialized skills.
  • Data Marts and Data Warehouses.  Unless you purchased a product from IBM, the early products were often purchased and managed by marketing and finance.
  • Business Intelligence tools.  Many companies still manage analytics and report development outside of Central IT.
  • CRM and ERP systems.  While both of these packages required Central IT hardware platforms, the actual application systems are often supported by separate teams positioned within their respective business areas.

The success of Shadow IT is based on their ability to respond to specialized business needs with innovative solutions.  The technologies above were all introduced to address specific departmental needs; they evolved to deliver more generalized capabilities that could be valued by the larger corporate audience.  The larger audience required the technology’s ownership and support to migrate from the Shadow IT organization to Central IT.  Unfortunately, most companies were ill prepared to support the transition of technology between the two different technology teams.

Most Central IT teams bristle at the idea of inheriting a Shadow IT project.  There are significant costs associated with transitioning a project to a different team and a larger user audience.  This is why many Central IT teams push for Shadow IT to adopt their standard tools and methods (or for the outright dissolution of Shadow IT).  Unfortunately applying low-cost, standardized methods to deploy and support a specialized, high-value solution doesn’t work (if it did, it would have been used in the first place).  You can’t expect to solve specialized needs with a one-size-fits-all approach.

A Shadow IT team delivers dozens of specialized solutions to their business user audience; the likelihood that any solution will be deployed to a larger audience is very small.  While it’s certainly feasible to modify the charter, responsibilities, and success metrics of a Centralized IT organization to support both specialized unique and generalized high volume needs, I think there’s a better alternative:  establish a set of methods and practices to address the infrequent transition of Shadow IT projects to Central IT.  Both organizations should be obligated to work with and respond to the needs and responsibilities of the other technology team.

Most companies have multiple organizations with specific roles to address a variety of different activities.  And organizations are expected to cooperate and work together to support the needs of the company.  Why is it unrealistic to have Central IT and Shadow IT organizations with different roles to address the variety of (common and specialized) needs across a company?

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