By 2020, millennials are projected to make up 75 percent of the global workforce. As the influx of younger workers continues to grow in the enterprise, organizations are being forced to adapt to this changing workforce, with a need for simplicity, ease of use and mobility.
Millennials were raised with portals to unlimited information in their pockets, able to Google anything on a whim. Generation Y, now increasingly finding themselves in leadership roles as Gen Z enters the working world, seeks transparency and thrives in an environment where they are challenged to pursue their own core purposes (all while maintaining a good work-life balance).
So, how can enterprises adapt to the millennial workforce while simultaneously increasing operational productivity, reducing costs, and gaining a competitive advantage? It starts with how to manage data.
Today’s Data Deluge
Data is one of a company’s most valuable assets, however increased use of digital technology is creating massive amounts of data from cloud, mobile, IoT, social media and more. Because of this overflow, many companies are simply streaming data into “lakes” – a holding ground for native data they haven’t had time to process, and that is often unmanaged. But these large volumes of data can also open up new business opportunities for companies that are able to quickly uncover orfind hidden value in massive amounts of data. When strategically managed, information can be transformative.
Having an enterprise data management strategy with a perspective that encompasses security, hybrid deployment, flexible integration methods, automated workflow and trusted analytics is key. Without a solid data strategy just understanding where data came from will be a challenge, and using it to enable better business outcomes will be nearly impossible.
A Structure to Enable Transformation
To create a solid data management strategy, there are three key areas every team needs to focus on:
1. Data consumption: Organizations are creating, collecting and managing massive amounts of data, but most are unable to rapidly analyze, understand and act on the data. To avoid moving too slowly, losing sales and running afoul of regulations, they need solutions that can modernize their data management infrastructure to enable employees to work with any type of data from any source – both on premise and in the cloud. These data management solutions make it easier to cleanse and integrate enterprise data so it’s always available to meet business needs.
2. Data quality: Dirty data can not only interfere with decision making, but it can also hinder the ability to introduce new technology to enhance enterprises processes and performance. Data that is of unknown origin, quality and context is untrustworthy for use in initiatives to increase revenues, decrease costs, and manage risk. In fact, rather than being a strategic corporate asset it becomes a business liability.
The ability to discover, measure and continuously improve data quality is the cornerstone of any successful enterprise information management program. There are several ways to understand and continuously analyze the trustworthiness and quality of enterprise information. For example, setting up data processes that provide an automated, turnkey way to improve data quality and integrity. Doing so can speed up the rollout of new technology because the data will be trusted from the start. Additionally, users can collaborate on data management tasks, such as:
- Running what-if analyses of poor data-related costs
- Establishing data definitions, rules and standards to use across the enterprise
- Monitoring and sharing data quality metrics over-time for continuous improvement
With full visibility into data quality, business users in your organization can see how their data measures up against information governance rules and standards.
3. Data security: Data is a risk to a business if it’s not effectively governed. Today, the big question for any Chief Data Officer, CISO, CSO or CIO when evaluating new technology is, “Can I trust it and use it?” With increased innovation often comes increased risk. The complexities of the digital economy combined with the emerging “hacker industry” are significantly increasing the threat to organizations.
Specifically, interconnectivity between companies and businesses across the globe has led to unprecedented exposure of business-critical systems and applications to online threats, making these systems an attractive target for hackers and criminals. As the corporate landscape expands to include applications on premise and on the cloud, the need to secure data across this hybrid infrastructure grows.
Companies can no longer rely on just firewalls and barricading their perimeter; they must also look to use 360-degree correlation analytics across network, endpoints, application and data to better address threats. The increasing availability of connectors/ APIs to access data allows organizations to aggregate data from more sources, enabling these correlations.
Conclusion
In digital business, the need to ensure that enterprise data is trusted, complete and relevant is more urgent than ever and foundational to the success of an enterprise. Ultimately, beneath the surface of all the data an enterprise collects is information to help drive customer success. As millennials continue to take over the workforce, they bring with them new ideas and processes that are helping companies on their digital transformation journey.
About the Author
Kristin McMahon is a Senior Director of Product Marketing for Enterprise Information Management (EIM) Solutions at SAP. McMahon is responsible for leading the product marketing cross-activities of the SAP Database and Data Management (DDM) portfolio in addition to leading the product marketing team for SAP Enterprise Information Management (EIM) solutions. Prior to SAP, McMahon held product marketing and business development positions at Business Objects and Firstlogic, Inc. McMahon brings over 15years of broad Enterprise Information Management experience to SAP. Her background in information quality, enterprise software and data governance, and analytics enables her to provide expertise in the database and data management market to global organizations. McMahon holds a Bachelor of Science degree from the University of Wisconsin and a Masters of Business degree from St. Thomas University School of Business.
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