How to create a trusted data environment in 3 essential steps

How to create a trusted data environment in 3 essential steps

We are in the era of the information economy. Nowadays, more than ever, companies have the capabilities to optimize their processes through the use of data and analytics. While there are endless possibilities wjen it comes to data analysis, there are still challenges with maintaining, integrating, and cleaning data to ensure that it will empower people to take decisions.

Bottom up, top down? What is the best?

As IT teams begin to tackle the data deluge, a question often asked is: should this problem be approached from the bottom up or top down? There is no “one-size-fits-all” answer here, but all data teams need a high-level view to help you get a quick view of your data subject areas. Think of this high-level view as a map you create to define priorities and identify problem areas for your business within the modern day data-based economy. This map will allow you to set up a phased approach to optimize your most value contributing data assets.

The high-level view unfortunately is not enough to turn your data into valuable assets. You also need to know the details of your data.

Getting the details from your data is where a data profile comes into play. This profile tells you what your data is from the technical perspective. The high-level view (the enterprise information model), gives you the view from the business perspective. Real business value comes from the combination of both views. A transversal, holistic view on your data assets, allowing to zoom in or zoom out. The high-level view with technical details (even without the profiling) allows to start with the most important phase in the digital transformation: Discovery of your data assets.

Not only data integration, but data integrity

With all the data travelling around in different types and sizes, integrating the data streams across various partners, apps and sources have become critical. But it’s more complex than ever.

Due to the sizes and variety of data being generated, not to mention the ever-increasing speed in go to market scenarios, companies should look for technology partners that can help them achieve this integration and integrity, either on premise or in the cloud.

Your 3 step plan to trusted data

Step 1: Discover and cleanse your data

A recent IDC study found that only 19% of a data professional’s time is spent analyzing information and delivering valuable business outcomes. They spend 37% of their time preparing data and 24% of their time goes to protecting data. The challenge is to overcome these obstacles by bringing clarity, transparency, and accessibility to your data assets.

Building this discovery platform, which at the same time allows you to profile your data, to understand the quality of your data and build a confidence score to build trust with the business using the data assets, comes under the form of an auto-profiling data catalog.

Thanks to the application of Artificial Intelligence (AI) and Machine Learning (ML) in the data catalogs, data profiling can be provided as self-service towards power users.

Bringing transparency, understanding, and trust to the business brings out the value of the data assets.

Step 2: Organize data you can trust and empower people

According to the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms, 2017: “By 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not.”

An important phase in a successful data governance framework is establishing a single point of trust. From the technical perspective this translates to collecting all the data sets together in a single point of control. The governance aspect is the capability to assign roles and responsibilities directly in the central point of control, which allows to instantly operationalize your governance from the place the data originates.

The organization of your data assets goes along with the business understanding of the data, transparency and provenance. The end to end view of your data lineage ensures compliance and risk mitigation.

With the central compass in place and the roles and responsibilities assigned, it’s time to empower the people for data curation and remediation, in which an ongoing communication is from vital importance for adoption of a data driven strategy.

Step 3: Automate your data pipelines & enable data access

Different layers and technologies make our lives more complex. It is important to keep our data flows and streams aligned and adopt to swift and quick changes in business needs.

The needed transitions, data quality profiling and reporting can extensively be automated.

Start small and scale big. A part of intelligence these days can be achieved by applying AI and ML. These algorithms can take the cumbersome work out of the hands of analysts and can also be better and easier scaled. This automation gives the analysts faster understanding of the data and build better faster and more insights in a given time.

Putting data at the center of everything, implementing automation and provisioning it through one single platform is one of the key success factors in your digital transformation and become a real data-driven organization.

Source: Talend