Traditional data management techniques are adequate when datasets are static and relatively few. But they break down in environments of high volume and complexity. This is largely due to their top-down, rules-based approaches, which often require significant manual effort to build and maintain.
In most organizations, data is collected from many different sources. It’s like a team mapping out local roads in an urban environment. Like the term “data,” the term “road” includes distinct types of road, including freeways, primary street roads, neighborhood streets and service roads.
Now, imagine an organization that collects information on a large urban area’s roads, but then keeps information on each different type of road separated from each other. It’s impossible to get a full picture of the entire roadway system and how traffic does (and doesn’t) flow.
That’s the issue with data in many organizations. While collection has increased rapidly, different types of data are kept “siloed,” with no ability to combine or cross-reference the data. It’s a huge issue. Data lakes lead to businesses not having the ability to get the most insight out of the data they have collected.
This is where those with data science degrees come in. Data unification allows the merging of data that is able to be mined for useful insights into past business activity or in creating predictive models.