The Impact of Data Growth on Enterprises

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Click to learn more about author Ibrahim Surani.

Data has changed the world we live in. Today, enterprises utilize data to introduce new business models, enhance customer experience, generate new revenue streams, create personalized services, and become more agile. This reliance is only increasing with time as the world experiences unprecedented data growth.

This data deluge has presented challenges in different aspects of Data Management:

1. Diversity of Source Systems

The digital explosion has resulted in the emergence of diverse data generators across consumer and enterprise landscapes. Amongst this mix, there are hundreds of cloud applications, smartphones, websites, and social media networks, with each source generating meaningful data. A survey conducted by CIO revealed that 20 percent of respondents feed their analytics and BI systems with data drawn from 1000 or more sources. And, on average, an organization consumes data from 400 sources.

The disparity in input formats and structures makes it extremely difficult for organizations to present data in a format that is useful for reporting and analysis.

2. The Upsurge in Data Volume

The global data creation is predicted to reach 175 zettabytes (ZB) by 2025, according to IDC. This is evident from a survey result that revealed an organization deals with 63 percent growth in data volume per month, on average, with 12 percent of respondents reporting 100 percent growth.

The firehose of data keeps getting turned up higher, necessitating companies to find a solution that efficiently processes large data volumes.

3. Transform Data to Improve Usability

To utilize the data generated by the systems present in the enterprise stack, organizations need to standardize, aggregate, filter, or sort data. Additionally, data has to be cleaned to ensure accuracy and remove duplications. Data transformation makes all of this possible!

Organizations opt for different approaches to convert data into a specified format before feeding it to the end-applications. The manual approach requires writing code to make clean, standardized, and enriched data. The other, more user-friendly approach is using a third-party, enterprise-grade ETL tool that contains built-in transformations to process data without writing any code.

As a demanding element of the pre-analytics process, transformation needs to be simplified and automated so businesses can use it to their advantage.

Organizations Must Be Prepared to Handle Data Growth

The growth in data volume and variety presents unique challenges in terms of Data Management. Without the right Data Strategy at hand, business leaders will find themselves with heaps of data that is difficult to utilize and analyze. This could result in organizations experiencing operational inefficiencies and, ultimately, a loss in revenue.

The growing flood of data stresses the need for a fast and scalable data integration solution that can extract data trapped in disparate systems and transform it into standardized analytics-ready information. Such a solution would capture the value contained in large data volume by converting raw datasets into value-adding reports and analyses that allow decision-makers to bring long-term improvements to underlying business processes.

Adding an integration solution to the enterprise stack will enable businesses to ride the technology trend and generate value from the creation of data.

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