While most companies recognize that their data is a strategic asset, many are not taking full advantage of it to get ahead. In this blog, we discuss the elements of a Data Strategy that will help you make decisions based on data analysis rather than intuition.
Companies know their data is a strategic asset and they want to utilize it to make smarter decisions; but the problem is – it’s complicated. Often data is scattered in silos, stuck in departmental systems that don’t talk well with one another, the quality of the data is poor, and the associated costs are high. In conjunction with responding to market pressures, most companies are going to prioritize the urgent, tactical, day-to-day needs over the long term strategic initiatives.
Moving towards a more data-driven culture is certainly attainable, and it starts with a Data Strategy. A Data Strategy is often viewed as a technical exercise, but a modern and comprehensive Data Strategy addresses more than the data; it is a roadmap that defines People, Process, and Technology. The exercise of creating a data strategy is one in which organization leaders take a deliberate look at:
A Data Strategy should lay out a detailed plan to mature in analytic capabilities and transition from making decisions based on hindsight to making decisions with foresight. A seen in the Gartner Analytic Ascendancy Model, the goal should be to move from using Descriptive Analytics (“what happened?”) to Prescriptive Analytics (“how can we make it happen?”).
Gartner Analytic Ascendancy Model
Let’s talk about how to get started with your own strategy. We’ve helped dozens of companies with varying levels of analytical maturity and technical needs craft their Data Strategy. Each of the following elements have been identified through this experience and can be applied to any organization looking to get ahead with their data.
Data must address specific business needs in order to achieve strategic goals and generate real value. The first step of defining the business requirements is to identify a champion, all stakeholders, and SMEs in the organization. The champion of the Data Strategy is the executive leader who will rally support for the investment. Stakeholders and other SMEs will represent specific departments or functions within the company.
Next is to define the strategic goals and tie department activities to organization goals. It’s natural for goals to exist at the company and department level, but the stated goals for both levels should sync up. These objectives are most effectively gathered through an interview process that starts at the executive level and continues down to departments leaders. Through this process, we’ll discover what leaders are trying to measure, what they are trying to improve, questions they want answered, and ultimately, the KPIs to answer those questions.
By starting with the gathering and documenting the business requirements, we overcome the first roadblock to many IT or technical projects: knowledge of what the business is trying to accomplish.
With a good understanding of what questions the business is asking, we can turn to the next element: analyzing data sources, how that data is gathered, and where the data exists. It’s unlikely that all data will be available within the organization and that it already exists in a place that’s accessible. So, we need to work backwards to find the source.
For data that can be found in-house, we note the source system and any roadblocks to getting assess to that data. We also need to determine whether the data has the right level of detail and is updated with the right frequency to answer the question effectively. For example, is the data private (especially in light of GDPR and CCPA)? Is it guarded by restrictions brought on by software licensing?
For data that isn’t available, we’ll note that here and pick it up in the next step. For example, a retail company may want to know how a brand is perceived before and after a big product launch. Let’s say the retailer has call center data, traffic counts for stores and online, and overall sales and return numbers. That doesn’t measure how customers are feeling or what they’re saying about the brand. The retailer may choose to pull in social media data to measure sentiment. Call center data, traffic, and sales would be noted in this step, along with the opportunity to pull in social media data.
For one of our clients, we built a matrix that was slightly more complex than the following sample. We identified their business questions, understood the data needed to answer the question, and mapped each to a source system.
Walk through questions that need answers, document required data, and identify gaps in a source matrix:
Our first piece of advice is: don’t get caught up in the hype and latest technologies; focus on the business reasons for your initiatives. Building a flexible and scalable data architecture is a complex topic for which there are many options and approaches, so here are some important things to consider:
All of these considerations will go into an overall architecture. And, as with most designs, the more your requirements and future needs are taken into account, the more the solution will actually support the business.
Here is a conceptual diagram of the different types of analysis and storage that need to be captured through either technology or process. An architecture diagram will identify and represent all the source systems, the methods to ingest data and the landing spots for that data (data marts, data lakes and various types of data warehouses). It will also layer in processes like data governance and information security.
Conceptual Data Architecture
A Data Strategy should provide recommendations for how to apply analytics to extract business-critical insights, and data visualization is key. Many companies still rely on Excel, email, or a legacy BI tool that doesn’t allow interaction with the data. Often a tedious, manual process is required, and relying on IT to create reports creates a bottleneck.
Data visualization tools should make the data look good, but more importantly, make the data easier to understand and interpret. Some factors that should be considered when choosing a data visualization tool include:
As we’ve stated, becoming data driven requires more than just technology. In this stage we look at the people in the organization and the processes related to creating, sharing, and governing data. A Data Strategy is likely going to introduce more data and data analysis and maybe new tools. Based on this, it makes sense to look at the skillsets of the users to understand their strengths and where they’ll need support. Do they need training? Do you need to hire more people? Organizational structure should also be assessed – should analysts be aligned to a business unit or to IT? And how IT will support the business in their analytics needs? Even topics like employee reviews and incentive plans should be evaluated. After all, these can be used levers to encourage employees to use data in the way the organization is intending
When employees are handed new tools but not shown how to think differently about their jobs, the end result is unlikely going to change. Here’s an example of a training plan we created for a customer that includes every level of the organization.
Example Training Plan
When it comes to process, many organizations have unintentional roadblocks to utilizing their data in decision making. Business processes may need to be re-engineered to incorporate data analysis. This can be achieved by documenting the steps in a process and where specific reports are leveraged for a decision. We can also mandate that specific data be provided as rationale for a business decision. Recognition can also go a long way – when you gain a win that is based on new use of data, it should be celebrated and promoted to build internal momentum and encourage positive behaviors with data.
Data Governance is what ultimately allows enterprise level sharing of data and the oil that lubricates the machinery of an analytics practice.
A data governance program will ensure that:
We don’t look to a tool to solve data governance; it’s people work, and it has to happen. Data Governance takes leadership and sometimes navigating through difficult conversations.
Developing a data dictionary is a good place to start. A data dictionary is a living document in which all available end-user measures and dimensions are formally defined. During these conversations, misunderstandings about terms are identified and corrected.
The Roadmap is the culmination of all the work we’ve done to this point and what makes all our previous work actionable. We’ve identified all that needs to happen to bring you from where you are to where you’d like to go, but before getting started with any design, build, training, or re-engineering of a business process, it’s critical to prioritize the activities.
For each recommendation that will help bridge the gap from current state to the future state, define the feasibility and expected business value it will provide. The plan should prioritize activities that are easiest to implement but also provide quick wins to the business.
Other factors to include in the roadmap are:
The Roadmap should also contain a timeline that allows for celebration of incremental wins that are earned along the way.
Example Project Timeline
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