The challenges for organizations working with data have evolved in recent years. Most now have access to data. They also have the means to collect and analyze it.
The issue now is how to manage it.
Next to finding ways to reach actionable conclusions through data analysis, finding efficient ways to store and access massive databases is the top issue facing organizations. With millions of data points now collected on issues such as customer’s online activity, demographics and preferences, businesses have found that it’s not a lack of data that causes them headaches.
The issue now is what to do with all that data. Many organizations have found themselves with vast “data lakes” on their hands – data that is captured and stored but not in a condition where it can be used for analysis.
Data unification can provide an answer.
Data Challenges
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.
What Is Data Unification?
Data unification involves merging data from multiple sources and making them useful for developing business strategy.
Doing so requires a process of collecting, cleaning, de-duplicating and exporting millions of data points from multiple sources. It’s a task that requires human programmers and machine learning.
Data scientists write programs to direct software on how to collect, match and merge data. It’s a colossal task. In many cases, simply developing the right code to handle data unification can take months.
Automated systems then collect and interpret data from multiple sources, merging it into a cohesive data set.
Challenges In Data Unification
The complexity of data unification depends on the amount of data, of course. Organizations combining data from four or five sources and hundreds of thousands of data points have a relatively simple task.
Larger operations face a bigger challenge. For example, General Electric spent years on data unification for its procurement systems. Why? Because the global conglomerate has 80 different systems, which originally did not share information.
That meant a procurement officer looking to make the best deal could only look at data from her past records, not the price levels on the most current deals within all GE companies. While it took years to accomplish, GE now estimates it will eventually save $1 billion per year.
Managing and leveraging data is the biggest challenge for a number of organizations. That’s why the demand for data unification – and the data scientists who know how to get it done – is expected to continue to grow in the coming years.