Start with Data Governance or Go Home.

Data governance is the who, what, why, when, where, and how data will be distributed, managed, curated, and updated throughout the organization. Data governance is often one of the things often overlooked in organizations.  As managers start to realize that they have valuable data and need to understand how that data can be used to further the goals and the ambitions and the vision of the organization, they often forget about data governance. Data Governance is really the rule of law internally of the structure and the processes of how information is shared & managed across the enterprise. I've seen over the years and with various companies that I've worked with that information is often very siloed into different departments. Data can also be found in different regions of the company, different divisions, and even different parts of the globe.

 There are a whole host of issues that really need to be cleaned up and worked out before any significant amount of money and effort are really spent on making managerial analytics work to help drive revenue and drive profitability in an organization and solve other key problems. For data governance to work, senior leadership in the organization should take the lead that starts to put together the “rules of the road” of how data will be governed. It usually starts with the formation of a data governance team. The data governance team is really the key team that manages and works through data issues on an ongoing basis. It is often formatted as a cross functional team made up of people from across the organization who have interests how data is managed across the enterprise.

 The data governance team is the team that referees issues as they come up, and invariably issues will come up in context to new data streams where we need to ask ourselves how we want new data to be managed so it is accessible across the company. Whatever the data issue, the data governance team should meet on a regular basis, to deal with data issues/conflicts that need to be resolved. Once you have a fairly well articulated strategy for handling and managing data within your organization, and have set up a working data governance team, your off to the races.

 One the governance team is operational; the next most important thing issue is inventorying all of the data that you have within the enterprise.  Once you find it, you've got to catalog it, understand what it is, including what legacy system it lives in.  To find pockets of valuable data – look for places where there's already a certain amount of data analytics work that's going on. That work might be isolated to certain departments. For example, the marketing team is doing something around data analytics related to digital campaign performance.  Some departments are farther along in managing their data.  So, you really need to understand who's doing what and with what information or data are they doing it with. This sounds obvious, but you'd be surprised how often it's a surprise to the CEO or another senior executive that there's a lot of great work being done in one area of the company but it’s not necessarily being filtered up or down or out to other parts of the company. This is about data accessibility.

 The other issue for data governance is when new streams of data come into the organization. The data governance team should look to understand the source of the new data, how often is it updated, and how to make it available the organization. And this might be a new unstructured data source, it might be a new structured data source, wherever, often with when you when companies acquire each other.

 Data governance is the center of gravity for all things data analytics – it’s the glue that keeps everything together and break down data silos.  Again, it's important implement some level of data governance before a whole lot of money is spent on developing data/managerial analytics horsepower. or data governance is in the book and we appreciate your time today.