Data Strategy and Analytics Strategy: Why Both Are Essential to Analytics and BI

Lyndsay Wise
October 17, 2019

Too many businesses in the past built their analytics projects by delivering reporting and dashboards across the organization, without focusing on the underlying data that feeds these systems. Consequently, to gain value from analytical outputs, these organizations have needed to take a step back to identify their data management needs and the type of data required to feed their reporting and dashboard solutions. Aside from additional time and resources required to re-evaluate the way data and analytics are tied to one another, additional data preparation activities were needed to ensure that analytics access led to valuable outcomes.

Today, we see market acquisitions and analytics vendors investing in the ability to provide a full stack – both analytics and data management – to ensure that companies can create a cohesive approach to analytics by leveraging a single platform for their analytics needs. In essence, the market is catching up with organizational needs and enabling organizations to take a more cohesive look at how their data and analytics investments are linked together.

At the same time, leveraging a platform approach to analytics and data management is no longer enough. Businesses need to develop a strong set of strategies that can be aligned with business outcomes. Developing a data strategy to manage data challenges and take advantage of opportunities, and an analytics strategy to align business goals with analytics delivery are essential to this approach. Organizations also need to align, both to ensure that data assets can be leveraged by business leaders and that actionable outcomes are possible.

With upper management support, data initiatives take center stage and the organization on the whole becomes more dedicated to analytics outcomes and becoming data driven.

Understanding the Importance of a Data Strategy

The market is starting to understand the importance of data independent of technical requirements and data integration projects. There is finally a lot of talk about the business value of data and how data can be leveraged to drive business value. Doing so effectively requires the creation of a data strategy. This strategy is not just about what data management investments and initiatives are required within an organization, but how an organization can leverage its data assets to become data driven.

The ability to tie data management to a defined strategy creates strong data validity within the organization. With upper management support, data initiatives take center stage and the organization on the whole becomes more dedicated to analytics outcomes and becoming data driven.

Evaluating and Developing an Analytics Strategy

An analytics strategy looks at how decision-making is defined within the organization and what overall business challenges it faces. How to address those challenges through analytics is usually how an organization develops its initial analytics strategy. As time goes on, the analytics strategy is tied more tightly to the overall business goals of the organization. To gain value from analytics, there needs to be quantifiable outcomes.

This means that decision-makers need to identify what they want to achieve and how they are going to measure success. This goes beyond the creation of KPIs and towards a more holistic approach to analytics and BI. This ensures that outcomes can be measured, the company is meeting its targets, and that challenges are being addressed proactively. In essence, an analytics strategy needs to be tied to overall organizational goals.

Aligning Both Strategies to Create Strong Analytics Outcomes

This is why both analytics and data strategies need to be tightly aligned. Data provides the backbone of any analytics narrative. If managed properly, data assets can be used to identify trends, look at performance issues, and drive change within the business. But this needs to be aligned with use.

How people interact with technology, the tools they use, and how they interact with systems to gain analytical insights provides the connection between data and outcomes. Without the two together, an organization’s analytics initiatives will not be successful over the long run. And without a way to leverage data assets, the time spent on data management will not show rewards. Getting the most potential from data and analytics requires an overarching strategy that ties in what information is needed to drive business value and create opportunities through data access.

Drive better business performance with a practical data strategy.