Data enrichment – a force multiplier in a big data environment

As you have probably heard or read, IBM’s Marketing Cloud recently published that “90% of world’s data today has been created in the last two years alone.” Growing daily at 2.5 quintillion bytes of data daily, this number will only explode over the next few years.

This may seem impressive, but much of it is simply raw data. Nonetheless, you may point out that with all this data we are advancing technology, improving outcomes, enriching lives and making better decisions. This is true. However, how vastly improved these outcomes could be if all this data was enriched? Think about the concept of enriching data to make data truly an asset to an organization, project, or research. It also shows the common importance of proactively using the data in numerous ways.

Of course, there are varying levels of data enrichment and they can work in different ways.  There is a plethora of tools that are used during this process, ultimately the end goal is data refinement. It may be as simple as correcting minor data entry errors, typos or misspellings using algorithms. Following this rationale, data enrichment tools could add information to basic data tables.  Another example of data enrichment is through extrapolating data. This is done using data methodologies such as fuzzy logic, database administrators or data scientists can generate more from a given raw data set.

In the world of big data, data enrichment has recently materialized, making significant improvement in business value of integrated data. As we’ve noted recently about ETL vendors and developers, they traditionally just move data from source to target unaltered. It’s now time to improve outcomes using data enrichment processes and techniques. However, an important note to make – the business should lead and manage enrichment definition.

You might be asking how can your business or process add value to your data and support greater decision making across data enrichment.  Essential data enrichment services can easily be attained from some great providers such as Lusha, Crunchbase, Trillium, etc.  As you move down this path of interviewing and selecting a data enrichment partner, it is important to clearly communicate the business goals of your organization to your prospective partner.

Collecting the benefits

So now you can enrich the data that you are collecting, but are you gleaning the benefits from this data you’ve stored? You’ve made the right decisions to ensure you harvested and stored your data efficiently and effectively as possible. Sure, this is vital to your business or project.  However, the real value is how you augment that data is where you will ultimately see it’s benefits. Data enrichment counts the most when you’re able to gain better understanding and intelligence into your business which helps improve decisions, stimulates customer engagement and improves your bottom line.

Ultimately, your goal is to boost the data that you are currently storing. Whether it is at the point of capture or after the data is accumulated, adding insights from a thorough information source is where the real value is gained.  Having the insight at this point you will acquire a better and more complete understanding of your prospects and target market. Essentially, you’ll learn more about your market by appending business information to the records that you capture and store, pinpointing key sociodemographic groups of business prospects or improving efficiencies across your business units.

Taking enrichment to the next level – machine learning

Sure, using precision algorithms is one common way to enrich data, but how about a higher level or faster process of data enrichment using machine learning? Most commonly when talking machine learning, we associate building predictive models generating insights that directly help business managers to make decisions. Using machine learning as part of a data enrichment application typically is used to add useful tags or other material to existing data so that that data can be used more effectively. During these processes, the function of machine learning happens at the earlier stages of analyzing or enriching the data. When working in big data environments, sometimes the volume of data collected is so large that it is not practicable to manually add the kind of classifying information to the data. Thus, the dependence of machine learning for these enormous tasks.

It’s important to remember, as data usage increases, data quality will only continue to evolve.  Especially with the accelerated improvements in the AI space.