Part 1 in the Trusted Data Imperative series
Business needs more trusted data – clearly this is true. But what are the challenges to managing data with care and diligence as you would any other key asset? Data shows its value when it gets used, when it moves from A to B, as it flows. Consider data as the new “currency of business.” As with money, there can be multiple endpoints and intermediaries that need your visibility so you can be sure the money is from reliable sources, not double-counted, lost, or even counterfeit.
Similarly, these concerns in the cash sample also reflect the demands for data security and privacy when the data is flowing. For example, you need to safeguard access to data and conceal people’s identities to avoid unlawful identification. Trusted data should be accessed only by people and machines with the right credentials in order to respect the privacy of the individuals the data represents.
In other words, you need to TRUST your data. Whether running enterprise software or data analytics projects, if the underlying data can’t be trusted, you will soon run into issues.
In enterprise terms, this rapidly moves into a discussion around data governance. Even if you have been working a long time to properly manage data – its quality and lineage – with respected stewards and custodians across your enterprise, there are some new challenges emerging.
- Data protection and consent for its use, demanded by citizens and consumers, and enforced by regulations such as GDPR, are new attributes impacting the value and quality of data.
- Change adds yet another dimension. Gone are the days of a static landscape with defined data flows between established processes inside the corporate firewall.
Today business is highly flexible, with new data sources and an ever-increasing number of data consumers inside and outside the organization. There is a huge increase both in the supply and the demand for data. Trust in data also means establishing visibility in endpoints and “intermediaries” with broader visibility and cooperation across systems
Moreover, trusted data produces more value when it’s combined with other trusted data from different reliable sources. When data moves around, this adds to the complexity, and stronger data security is required. Privacy protection is top of mind for businesses around the world, especially now. The European Union General Data Protection Regulation (GDPR), which went into effect May 25, 2018, imposes steep fines to companies that don’t manage data in compliance with its security and privacy rules.
But protecting and governing data is no trivial task. Data is everywhere and seamlessly flows through networks and systems – whether applications, databases, data warehouses, file systems, or edge and mobile devices. Nobody wants to be the next company to suffer a data breach that exposes billions of sensitive customer records and face the prospect of massive brand equity and financial losses. But how can we achieve trust with data?
The following steps enable you to meet your expanding data-management requirements:
- Establishing a trusted data governance foundation: Define and document data and security policies, business rules, master data definitions, business terminology standards, and enterprise architecture models; discover and document where data and content reside; and identify and document what data and content are subject to which internal policies and external regulatory requirements.
- Driving business trust with always-accurate data: Cleanse, match, consolidate, and enrich data to comply with corporate standards; continually monitor and measure data quality against validation rules, display metrics scorecards, and quantify the financial impact of poor quality; and manage and synchronize master data across applications.
- Setting “trust” as a priority across the whole data lifecycles: Maintain the system by managing the content lifecycle and its association with data, processes, and applications; implement access controls, data anonymization, and encryption; establish and communicate data owners and stewards; embed validation checks into business process and data entry workflow; and implement archiving, retention, and deletion policies and rules.
Enable agile data operations across the enterprise
For example, you can take advantage of a data solution that centralizes landscape orchestration and governance, metadata management, and lifecycle management. Users control the flow of data by adding operators that can, for example, prepare data as it is being ingested, govern data across all data stores (whether on premises or in the cloud), and define data flows for machine learning models.
Importantly, these operators are containerized and can independently scale on the fly. These solutions help users turn their data into a strategic asset, delivering information with excellence-supporting capabilities that enable companies to understand, integrate, cleanse, manage, associate, and archive their data to optimize business processes and analytical insights.
Data also needs protecting
You also need to protect your data by masking and encrypting it, but also anonymizing it in real time without duplicating the data. This means your business data is protected against unlawful privacy disclosure right at the source, making it easier to comply with regulations such as GDPR.
With SAP HANA Data Management Suite, you get all the technology assets you need to achieve the data quality, consistency, and protection you require across your entire enterprise. This is important for all your data, and especially when combining it with valuable business data that often resides in your SAP landscape. Stay tuned for my next blog in this series coming next week.