Businesses need to think differently about data management in the digital age

Companies across industries are racing to embrace digital transformation in an effort to stay competitive and stimulate innovation and business growth. In fact, according to IDC, by 2020, 50% of the global 2000 will see the majority of their business depend on their ability to create digitally-enhanced products, services, and experiences.

But digital transformation comes with challenges, particularly around data management. Data is one of a company’s most valuable assets, but increased use of digital technology is creating massive amounts of data from cloud, mobile, IoT and more, resulting in a data deluge.

As organisations enter this digital transformation period, one of the biggest challenges for IT departments is assessing current data management technologies, understanding what needs to be modernised and which technologies should replace existing technologies. The question IT departments should be asking themselves is: does my existing data management infrastructure meet the needs of what the new requirements are of this digital age?

>See also: Data management challenges and opportunities in 2018

Here are four considerations for ensuring trust in data, extracting meaningful insights and governing it properly.

Assess existing technology mix

Before considering new technologies, companies should look at their current infrastructure, and decide whether it’s supporting their data. They must come to terms with whether their current technology is stunting growth, reducing profits or even increasing risk. In the end-to-end data platform realm, if companies bring their current data problems along with them, they lose the chance to realise the long-term benefits of digital transformation.

Among the key service layers of an enterprise digital platform is the data layer, a multi-tiered and multi-tenanted data discovery and profiling environment with applied intelligence to derive business value and meaning.

>See also: Operating a successful data management strategy

This ability to extract the maximum value from data is critical – more than 90% of organisations believe that data and analytics is a competitive differentiator, yet fewer than one-quarter of top managers involved with data and analytics state that they have been able to extract greatest possible value from data. To fully understand an infrastructure’s ability to support data, companies must evaluate their existing technology to determine whether it is sufficient or if new solutions are needed.

Make the most of big data

Traditional data processing systems (e.g. relational data warehouse) may handle large volume of relational data, but may not be flexible enough to process semi-structured or unstructured data. These data sets are now so large and complex that we need new tools and approaches to make the most of them. Using a variety of data integration methods such as ETL, data virtualisation, data federation, data streaming etc., IT organisations will be able to seamlessly exchange and integrate data between on premise and the cloud with enhanced support and connectivity for loading large and diverse data types.

Improve information governance

Information governance is immature at the majority of companies, with few forming data governance councils or appointing a chief data officer (CDO) to help sponsor and set the way data is managed to preserve its value and integrity. Companies need a way to define and implement information policies, definitions, and rules to best support efficient business processes and trust in reporting. But how can data stewards – as well as anyone in the organisation – quickly and easily view and understand information policies?

>See also: Connecting the dots: the hybrid data management arena

Data governance solutions help companies create and enforce data stewardship policies to comply with regulations such as GDPR as well as increase productivity and transparency as new data assets are brought into and moved between applications and systems. With these solutions, companies are better able to understand the impact of data quality on processes and enhance operational, analytical and data governance initiatives.

Leverage the cloud and self-service to cleanse data

Raw data can typically be spread out, corrupt, or in hard-to-use formats, making it difficult to understand. According to the New York Times, data scientists spend 50% to 80% of their time preparing digital data to provide business insight.

Business users need a solution that helps them easily and quickly transform data with built-in data profiling, data cleansing, data blending, and data enrichment capabilities for the best data curation possible.

In order to be of value to companies, the data must be cleansed to ensure its effectiveness. A company can’t apply one data cleaning process to ALL data, it must employ self-service in order to determine what needs to be “cleaned.”

>See also: The 4 secrets successful CDOs will know about data

Self-servicing data cleaning gets the data to the people who know what it should look like. Today, developers can embed data cleansing and enrichment services within their business processes or applications, which will ultimately lead to better decision making, streamlined business functions and a surge in productivity.

Data is at the core of digital transformation, but before considering new technologies, companies must analyse their current infrastructure to understand the state of their data.

Without accurate data, companies can encounter loss and suffer from a lack of brand value. However, leveraging accurate data can lead to drastic improvements in revenue and overall business growth.


Sourced by Kristin McMahon, senior director of product marketing for Enterprise Information Management (EIM) Solutions at SAP

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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...