The challenge for businesses across all industries no longer is the collection or analysis of data. Almost all large organizations have those abilities, now.
The challenge today is in how to manage that data in a systematic way that supports business strategy.
It’s an enormous task. Many organizations find themselves overrun with millions of data points, but with no set plan on how to strategically use it. That’s understandable with the rapid advancement of technology, where just a decade ago simply collecting data was the point.
But data-driven strategies have moved past such issues as system architecture, requirements gathering, testing and results measurement (to name a few). The emphasis now is on turning that raw data into useful information that supports organizational goals; a practice sometimes called data mining.
A data management strategy is the first step in the process of making the most of the information a company gathers.
What is a Data Management Strategy?
A data management strategy is a detailed plan on how to use the data available to an organization in such a way that it improves operations. That can range from finding efficiencies in production, increasing profit margins, eliminating wasteful activities and better serving both loyal and potential customers.
To prove effective, a data management strategy must be:
- Detailed and customized to the specific organization, not an off-the-shelf, “one size fits all” approach
- Actionable, not just data reports
- Flexible, because data strategy falls into the category of continuous improvement
- Connected to all phases of the organization, not just one or two
This isn’t easy. But getting there is possible by considering best practices in forming a data strategy.
Best Practices in Data Management
The following points, when taken together, provide an overview and a framework for where to begin developing a data management strategy. While challenging, the payoff in terms of business improvement is a worthy return on the investment.
Develop a Data Philosophy
Leaders in any organization cannot get to where they want to go with data unless they have a firm set of ideas on what their goals are with data. The SAS Institute, a North Carolina-based leader in business intelligence and data analytics, offers points to consider in developing a data philosophy.
- Identify: Understand the meaning of data no matter how it is structured, where it is located or the origin of the data.
- Storage: Store that’s easily accessible and understandable to those who need it, when they need it.
- Provision: Package data so that it can be easily shared
- Process: Combine data from disparate systems to create a unified view of the data for everyone across an organization
- Governance: Create mechanisms for storing and managing data, communicating these policies to everyone across an organization
Put Business Before Technology
In the go-go era of technology innovation over the past 30 years, business leaders have tended to implement the latest technology without a clear idea of how it will support business strategy. In establishing a data management plan, it’s important to make business strategy the priority. By ranking projects based on business goals, technology can then be considered from the standpoint of what is needed to support achieving those goals.
Develop Consensus
As with any large-scale initiative, data management requires everyone involved to be on board with the approach. Accomplishing this typically means getting all the stakeholders in the same room: executives, business managers, development teams and production teams. In some cases, organizations hire chief data officers to bridge the divide between business concerns and technology. Whatever the approach, it’s essential to have all involved understand both the direction of the strategy and the business goals that data management supports.
Make Data a Corporate Asset
Part of the strategy behind collaboration and consensus is to end the “siloed” nature of data in many organizations. As it stands, departments within large organizations often view data as pertinent only to their operation. A broader view is needed to develop a proper data management plan. All data should be examined through the lens of being a corporate asset and in terms of how it can support an organization’s overall success.
Collect Diverse Data
Another pitfall of data collection is only focusing on one or two areas. As pointed out by Forbes, true cross-functional analysis requires pulling data from a wide variety of diverse sources. For example, including social media data when analyzing customer interaction with a company’s products or services.
Track Performance
Tracking and measuring the performance of the data management strategy is key to continued success. As markets change and business strategy evolves, the data management plan must evolve with it. This can involve creating data logs to track performance and using automated systems to handle much of the work. But ultimately the people involved with the plan must, on a regular basis, consider these findings and decide what changes need to be made.
