5 essentials to transform your enterprise into a data-driven organization

Automation powers revolutions.

Throughout history we have seen automation drive step functions in efficiency and productivity, across industries, across cultures, across centuries.

The time is ripe now to bring automation to bear on data processing. Let’s talk about why.

The benefits of making data-driven decisions are vast and well understood. A quick google search will reveal an endless number of statistics and figures pointing to that.

Here’s one of them:

“Data-driven organizations are 23 times more likely to acquire customers.”

— Mckinsey Global Institute

Here’s another:

“Insight-driven businesses are growing at an average of 30% each year; by 2021, they are predicted to take $1.8 trillion annually from their less-informed industry competitors.”

— Forrester

We can keep going, but the point is clear. Data-driven = more customers, profits, market share etc.

So every time data is created, whether it be by IoT edge devices, marketing campaigns, company orders, or a myriad of other data sources, your enterprise gains more potential for data-driven value.

Given the tremendous investments made in data infrastructure as well as in data analytics software and talent over the past few decades, the modern enterprise is poised to put data at the heart of every strategic business decision.

So if we have the raw data and the analytical capabilities what is the problem?

It’s simple: the bottleneck to becoming data-driven is no longer the availability of raw data; and it’s no longer the availability of analytics capabilities; the bottleneck is the painful, repetitive, manual work of actually transforming raw data into usable data — usable data that can power analytics and data-driven decisions. We call this manual work data processing. Data processing includes manually mapping source schemas to destination schemas, manually deduplicating records, manually sorting through data to find and obfuscate PII, and a myriad of other repetitive manual tasks — all done in the pursuit of usable data.

Because of this bottleneck, tremendous amounts of value that could be derived from enterprise data is being left on the table!

The Power of Automation

So what do you do when your ability to capture a massive opportunity is bottlenecked on a manual process?

In telecommunications, we brought automation to the table. When you pick up the phone to make a call, you no longer talk to an operator; you’re connected automatically.

In financial services, we brought automation to the table. When you want to trade an equity, you no longer call someone on the floor of the NY stock exchange; you just click a button and the trade happens automatically.

In manufacturing, we brought automation to the table. When you want to build a car, you don’t drive around town collecting parts and then assemble them by hand; you utilize automated supply chains and factory floors.

All of these industries faced the same dilemma; manual processes that were

• Core to the business
• Repeatable with modern technology

And all of these industries were revolutionized — by automation.

That’s exactly what Data Process Automation (DPA) is: an opportunity to revolutionize your enterprise.

The impact of Data Process Automation

Bringing automation to your data processing can drastically reduce the time and effort needed to get from raw data to usable data. How will that revolutionize your business?

1. It will increase the number of data-driven initiatives you can execute. By lowering the amount of time, and subsequently the cost, of executing a data-driven project you can try more of them. That means a more diversified portfolio of data-driven projects, less risk when some of them fail to deliver results, and higher likelihood of positive ROI on your data efforts.
2. It will provide your analysts with better, more consistent data quality. That means no more dealing with the concept of “garbage in, garbage out”. Your results and conclusions will be backed by data you can trust.
3. It will empower your data scientists, analysts and engineers. Employees will no longer find themselves frustrated by looking for data, waiting on data and cleaning up data, and instead can devote their energies to the work they really enjoy: deriving insights from data.

So finally, what are the five essentials of data process automation?

The 5 pillars of Data Process Automation

1. Automatic data movement

Getting data from point a to point b sounds simple enough, but legacy ETL tools make it surprisingly difficult, requiring both knowledge of the complicated platforms and the creation of ad-hoc pipelines. Our DPA platform moves data via one dead-simple API endpoint and intuitive user interface.

2. Automatic data quality

Deduplication is a problem that seems to be as old time, one that many analysts must be shocked still exists. “Shouldn’t they have come up with a way to fix this by now?” is a question anyone who has had to manually dedupe a data set has asked in frustration. We think they should have, so we did. Our DPA platform provides an incredibly simple way of understanding whether two records are the same. This can be integrated into any data integrity workflow.

3. Automatic data modeling

Here’s where things get tricky. Every device collecting data has its own schemas creating outputs in different formats. Every ERP, HR, finance, marketing etc. system has its own schema as well. Wouldn’t it be nice to get them into the same format? Without hours of manual, tedious labor? Our DPA platform provides effective data modeling that provides the foundation for every team in your organization to create, use and share data and insights.

4. Automatic data unification

Now what if you wanted to actually unify all of those systems? In other words, what if your organization needed to take its gazillion systems with all of their gazillion formats and schemas and get all of the data that lives in them into one place in one format? Don’t worry about it, our DPA platform does that too.

5. Automatic data masking

Finally, even if your data is all standard and accessible you still need to be able to work with it and share it free of compliance concerns. Our DPA platform will automatically identify sensitive data, in both structured and unstructured formats, and mask it so that you can do the work you need to without worry.

Imagine a world

So, imagine a world where you can move and analyze quality data from system to system in a standardized format without compliance concerns. Now imagine a world where that happens automatically, so that, just like when you pick up the phone to call someone, how that call gets connected doesn’t even cross your mind.

That’s what data process automation is, that’s what will transform your enterprise into a data-driven machine, and that’s what we’re bringing to the table.

One final note — overhauling enterprise wide data processing systems can seem hard, maybe even impossible and we understand that. Datalogue’s DPA platform can replace your legacy ETL system — but it doesn’t have to. Every essential DPA pillar can be implemented to work in tandem with existing systems and processes so that implementation takes about 60 days, not years.