How to Implement Scaled Analytics for your Enterprise

So why do so many companies struggle with implementing enterprise analytics at scale? Here are some of the common challenges that we’ve observed:

• Data governance efforts often result in a technical bottleneck, instead of representing a solid foundation of business enablement. Finding the sweet spot of how to engage the larger organization and capture both technical and business context, as well as metadata, is vital to success.
• Enterprises are often too focused on the innerworkings of the data factory, which can cause a lot of distraction and uncertainty. Instead, your team should interact with a simple interface that generates insights for you.
• With so many tools on the market that promise to fix your data woes, some companies end up with an excess of applications and platforms. Constantly switching between applications and tracing back sources to find facts can be distracting and hinder business performance. Having a single source of truth closes the loop.
• Frequently, individual business units are focused solely on their own priorities and building their own metrics. While empowering employees is a positive, make transparency a baseline culture norm so you avoid creating data silos and disconnected solution islands that result in high maintenance costs.

If any of these challenges sound familiar, it’s likely time for your enterprise to start implementing an effective data governance strategy. How do you get started? First, start by scoping your data assets and determining who needs access to what information. Ensure you are comprehensive in talking to all different parts of the business, so you don’t create a siloed and incomprehensive data strategy. Next, consider your data analytics, BI reporting and business applications. Your company likely generates a lot of metadata, but how do you want to see that information? Who should see what information? What standards or rules do you need in place to protect the quality, safety and privacy of your data? As you start enablement across data products, monitor who is using what. Use that information to start building out your intelligent data marketplace and data collection process. Consider what operating model is needed to do this effectively. For example, task solution managers or even key users from the business to own and delegate curation. An IT center of excellence can moderate the platform, own processes and provide managed services to enable various business teams.

A typical quick-win approach is to pilot an integrated analytical workspace—a portal that provides a unified experience for all users to access your comprehensive set of data analytics applications. This workspace should benefit both the business and IT–enabling you to leverage the full potential of your data assets and apply enterprise data. If scoped and built effectively, your enterprise can accelerate business performance with faster, more accurate insights and increase ROI through better adoption. For example, to create efficient supply chains, use tools that promote transparency and optimal execution from manufacturing to consumption. Allowing companies to define and analyze their supply chains in a holistic manner enables end-to-end visibility.

With a productive work environment, your IT team can introduce self-service interfaces for the business to explore, build and share applications. Seamless platform governance enables process ownership, data stewardship and continuous monitoring. By consolidating your data product portfolio, you’ll achieve lower operations costs by enabling usability and removing redundancies. Simply put, data can be a hugely valuable asset for your organization, but only when governed, analyzed and used effectively to benefit all parts of your business.