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Integrate Your Big Data: Start Here

Author: Tobin Thankachen | 7 min read | August 4, 2022


‘Big Data’ is getting bigger. Every day every organization is deluged with vast amounts of information streaming from innumerable websites, devices, and apps. Buried within the mass are invaluable nuggets of corporately critical information, some of which can be game-changing for the enterprise. The challenge for every C-Suite is to parse through the mass to find those nuggets, then extract their value to enhance the company’s circumstances.

The first step requires sophisticated software that captures, cleans, and homogenizes all data so it can be evaluated from a single perspective. The second challenge requires sophisticated analysis programming that gleans the meaning of each data bit and attaches that to the meanings gleaned from other relevant data bits. The whole process presents several challenges; successful companies will overcome three key challenges, however, before they can embrace the full value of their Big Data resource.

Capture, Clean, Conform

Data emerges every second of every day. By the end of 2025, the daily volume of generated data will equal 463 exabytes, which is one billion gigabytes. In a single day. The burgeoning ‘Internet of Things’ will furnish 75 billion devices by that date, too, and by 2030, nine of every ten people (or 6,300,000,000 souls) will be digitally connected to some form of internet service.

Companies that want to maintain market share will need to manage and understand the values contained in this growing and increasingly complex reservoir of information. Three fundamental challenges stand in their way: how to capture it, how to clean it, and how to conform it so it can provide relevant, timely, and pertinent insights to enhance enterprise performance.

1. Capturing Big Data – Managing the Volume

Big data doesn’t arrive in uniform formats, systems, or programs. Instead, it shows up in a myriad of schemas and forms and in different computing languages, such as JSON and XML, most of which aren’t designed to connect well with other data formats. Capturing and storing all data formats requires a sophisticated, multi-layered architecture that is capable of comprehensively capturing and storing it, then managing it through all of its varied lifecycles.

In smaller businesses, a three-layer architecture might be appropriate, one that consists of a storage layer, a processing layer, and a consumption layer.

  • The storage layer would be the receptacle for all the data flowing in from the organization’s various operations, partners, customers, and suppliers. It would be compatible with their programming as well as the programming at the endpoints of the company’s computing.
  • The processing layer performs those processes that are core to the company’s activities. Depending on the business, processing could be done in batches, in real-time, or in a hybrid combination of the two.
  • The consumption layer produces the magic that makes the company unique, keeps it safe, and satisfies its customers. Coupled with analytics engines, the consumption layer provides the insights and information (the nuggets) used to direct decisions and move the enterprise forward.

Larger organizations might benefit from a more layered data management architecture depending on their work, the data they generate, and the data they collect. Their architecture may incorporate a data ingestion layer, query layer, analytics layer, and a security layer if those are responsive to the entity’s needs.

2. Clean Data – Address data quality concerns

As noted above, data comes in all shapes and sizes and can sometimes be malformed in its travels to the database. Even a tiny data error can cause significant problems when it’s reproduced at scale. In addition to poor data quality in general, the inconsistencies and complexity of data forms and shapes also add to the data quality concern. Organizations can’t understand the value of their data until those are analyzed, and they can’t be analyzed until they’ve been cleaned and processed. Processing will repair errors and ‘homogenize’ the digital information into a format that works with other data types, and it’s then that its value will be revealed.

3. Conform data – Integrate it to make it useful

Even after your data are repaired, homogenized, and formatted, they’re still not useful until their particular relevance is analyzed in context with the relevance of the others. Each data stream brings in different bits of information, and those bits must be arranged to reveal the more significant corporate insights that the organization seeks. For example, customer purchasing habits can identify best-selling products, yes, but that data can also inform production lines and supply chain investments. It’s not until all relevant information is gathered and synchronized around a specific organizational concern that its total value is realized.

Note, too, that there are as many available insights hidden in corporate data as there are queries to ask of it. Insights sought by one department may be irrelevant to another department even when the data they both use is the same. The data integration strategy focuses on ensuring that both departments have access to the data and that the information is stored and organized within the database in a way that each can find the answers they need using their own specific queries.

Datavail’s data integration specialists can develop the digital infrastructure to capture, clean, and conform corporate data to meet the specific customer’s needs. They can then set up the digital tools to harness the information in every department by replicating it when necessary, designing insightful dashboards to visualize data stories, and even embedding processes that integrate current data as it arrives in its native format.

Big Data is everyone’s future, and Datavail’s data integration professionals have the talent and insights to help your organization manage its Big Data. Reach out today.

To learn more regarding data integration modernization, download our white paper, “A Better Truth – Data Integration with Azure Data Factory (ADF).”

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