Information Governance: Talk the Talk

The Records Guru

If there is one thing I’ve found in today’s business world it’s that everyone can use all the buzzwords to keep up with the conversation at meetings and around the office, but if you really ask them about it everyone has a slightly different definition of what they actually mean. The problem is compounded when new buzzwords are coined, replacing some of the old ones or combining several old terms into something completely different. Now such looseness in language can be forgiven when you are just casually talking, but when you are talking about Information Governance the wrong definition can cost your organization real money or land you in Court.

I’m not naive enough to believe everyone in the world will actually start calling things what they are, but I would like to suggest that everyone in your organization should have a common understanding of what these things mean so you can make progress in dealing with them.To that end I would like to suggest a basic definition of some of the most commonly used buzzwords in Information Governance that you can use to start with:

Information Governance

Lets start at the top. The essence of Information Governance is getting trusted information to the right people at the right time. Look that definition over again and you will see there is a lot in going on in that sentence. That’s because a lawyer actually came up with it at a conference I attended awhile back and it really appealed to me.”Trusted information” means you can have confidence that this information is verified to be accurate and is the most recent. The “right people” means whomever has access to this information is authorized to do so and the “right time” means when they need it.

“Everyone in your organization should have a common understanding of what these things mean.”


Big Data

I’ve found that this is one that seems to have morphed over the past couple of years away from what we were all originally talking about. Big Data refers to very large data sets that an organization use to reveal patterns, trends and associations within it. It is very simply a massive collection of data that has been scrubbed clean of duplicates and errors that can be trusted to be used to give an accurate result of whatever your organization is looking for. Remember the old adage “garbage in, garbage out”? You avoid that issue when you create a Big Data repository. You might also have heard the term “Data Lake” or “Data Warehouse” as well. Depending on the size of your organization this could look like an ocean.

Business Intelligence

After you have created your Data Lake, you want to start fishing. Business Intelligence is the process of analyzing the data sets to reveal patterns, trends and associations within it. As we’ve come to expect there are multiple systems in the market to assist organizations reach their goals. One of the more interesting trends is that many of the newer technologies are introducing more user friendly user interfaces so that your don’t need a Data Scientist to get value from Business Intelligence. While there is a definite advantage to having those with the most experience in a particular business run queries, gain new insights and discover value that may not be enough. I’ve found that there is a great benefit in having a true Data Scientist with the analytical skill of working with data to create the computational models that can lead to real breakthroughs.

Machine Learning

Machine learning is the next step in Business Intelligence and refers to automated analysis using pattern identification and anomaly detection to sort through the data faster and more accurately than a human can. There is where it is even more critical to ensure that not only is your data clean and accurate, but the system is carefully programed so that the patterns and anomalies are correct. Think “garbage in, garbage out” at the speed of light.

Get it right and you can begin realizing benefits of Predictive Analytics, another buzzword concept, which can be used to extrapolate outcomes based on the patterns the data is showing. You can also use machine learning to assist you with Security Analytics. Big Data needs to be secured just like all of your organization’s information assets. The same machine learning process can also be used to detect security anomalies and generate responses based on established protocols faster than humans as well.

That should be enough definitions to give you a good basis going forward. I hope they work for you, but if not you need to come up with your own. Having a common set of definitions helps everyone’s understanding and that will benefit your organization.

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