The 5 Components of a Data Strategy

Because the idea of building a data strategy is a fairly new concept in the world of business and information technology (IT), there’s a fair amount of discussion about the pieces and parts that comprise a Data Strategy.   Most IT organizations have invested heavily in developing plans to address platforms, tools, and even storage.   Those IT plans are critical in managing systems and capturing and retaining content generated by a company’s production applications.  Unfortunately, those details don’t typically address all of the data activities that occur after an application has created and processed data from the initial business process. The reasons that folks take on the task of developing a Data Strategy is because of the challenges in finding, identifying, sharing, and using data.  In any company, there are numerous roles and activities involved in delivering data to support business processing and analysis.  A successful Data Strategy must support the breadth of activities necessary to ensure that data is “ready to use”.

There are five core components in a data strategy that work together as building blocks to address the various details necessary to comprehensively support the management and usage of data.

Identify          The ability to identify data and understand its meaning regardless of its structure, origin, or location.

This concept is pretty obvious, but it’s likely one of the biggest obstacles in data usage and sharing.  All too often, companies have multiple and different terms for specific business details (customer: account, client, patron; income: earnings, margin, profit).  In order to analyze, report, or use data, people need to understand what it’s called and how to identify it.  Another aspect of Identify is establishing the representation of the data’s value (Are the company’s geographic locations represented by name, number, or an abbreviation?)  A successful Data Strategy would identify the gaps and needs in this area and identify the necessary activities and artifacts required to standardize data identification and representation.

Provision       Enabling data to be packaged and made available while respecting all rules and access guidelines.

Data is often shared or made available to others at the convenience of the source system’s developers. The data is often accessible via database queries or as a series of files.  There’s rarely any uniformity across systems or subject areas, and usage requires programming level skills to analyze and inventory the contents of the various tables or files.  Unfortunately, the typical business person requiring data is unlikely to possess sophisticated programming and data manipulation skills.   They don’t want raw data (that reflects source system formats and inaccuracies), they want data that is uniformly formatted and documented that is ready to be added to their analysis activities.

The idea of Provision is to package and provide data that is “ready to use”.   A successful Data Strategy would identify the various data sharing needs and identify the necessary methods, practices, and tooling required to standardize data packaging and sharing.

Store               Persisting data in a structure and location that supports access and processing across the enterprise.

Most IT organizations have solid plans for addressing this area of a Data Strategy. It’s fairly common for most companies to have a well-defined set of methods to determine the platform where online data is stored and processed, how data is archived for disaster recovery, and all of the other details such as protection, retention, and monitoring.

As the technology world has evolved, there are other facets of this area that require attention.  The considerations include managing data distributed across multiple locations (the cloud, premise systems, and even multiple desktops), privacy and protection, and managing the proliferation of copies.   With the emergence of new consumer privacy laws, it’s risky to store multiple copies of data, and it’s become necessary to track all existing copies of content.  A successful Data Strategy ensures that any created data is always available for future access without requiring everyone to create their own copy.

Assemble         Standardizing, combining, and moving data residing in multiple locations and providing a unified view.

It’s no secret that data integration is one of the more costly activities occurring within an IT organization; nearly 40% of the cost of new development is consumed by data integration activities.  And Assemble isn’t limited to integration, it also includes correcting, standardizing, and formatting the content to make it “ready to use”.

With the growth of analytics and desktop decisioning making, the need to continually analyze and include new data sets into the decision-making process has exploded. Processing (or preparing or wrangling) data is no longer confined to the domain of the IT organization, it has become an end user activity.  A successful Data Strategy had to ensure that all users can be self-sufficient in their abilities to process data.

Govern           Establishing and communicating information rules, policies, and mechanisms to ensure effective data usage.

While most organizations are quick to identify their data as a core business asset, few have put the necessary rigor in place to effectively manage data.  Data Governance is about establishing rules, policies, and decision mechanisms to allow individuals to share and use data in a manner that respects the various (legal and usage) guidelines associated with that data.  The inevitable challenge with Data Governance is adoption by the entire data supply chain – from application developers to report developers to end users.  Data Governance isn’t a user-oriented concept, it’s a data-oriented concept.    A successful Data Strategy identifies the rigor necessary to ensure a core business asset is managed and used correctly.

The 5 Components of a Data Strategy is a framework to ensure that all of a company’s data usage details are captured and organized and that nothing is unknowingly overlooked.   A successful Data Strategy isn’t about identifying every potential activity across the 5 different components.  It’s about making sure that all of the identified solutions to the problems in accessing, sharing, and using data are reviewed and addressed in a thorough manner.