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TDWI Upside - Where Data Means Business

5 Key Considerations for Data Management Success

Why your organization must streamline its data strategies to extract more value from your data and enjoy other tangible benefits.

Data can provide key insights that help your organization improve operations, processes, decision-making, and customer experiences while gaining a competitive edge in your industry. These benefits combined with growing data volumes are driving businesses to become more data-driven. It isn’t easy. As of last year, little more than one-quarter (26.5%) of businesses reported having successfully established themselves as data-driven companies.

For Further Reading:

3 Data Management Rules to Live By

How to Build a Data Culture

To Be a Data-Driven Enterprise, Become Data Literate First

Let’s take a closer look at some of the most common challenges organizations face when adopting a data-centric approach and associated management strategy and how they can be addressed.

Top Data Management Challenges

All organizations have unique approaches to data management and face individual hurdles, but there are common factors that often hold businesses back when shifting to a data-driven mindset.

  • Data illiteracy. Many companies don’t have employees with the necessary skills to drive data-centric initiatives and fully leverage data, leading to missed opportunities and insights.

  • Poor data quality. Data teams working with poor data quality will suffer from inaccurate analytics that can misinform decision-making. Fresh, clean data is a must for organizations.

  • An absent data culture. Establishing a data-driven culture can only succeed with top-level (C-suite) commitment. More often than not, support from executives falls short, leading to failed initiatives.

  • Lack of scalability. When only a portion of data teams (or the business overall) can access the company’s central system due to a lack of scalability, data operations and innovation are often limited. Legacy systems and databases are commonly unscalable.

A Road Map to Data Management Success

Luckily, there are several tactical steps your organization can take to effectively leverage its data and become more data-driven overall. These include:

Embrace a modern data stack. Simplify and streamline your data management and analytics capabilities with a top-level, consolidated data approach. This is instrumental in helping determine where to invest to reduce costs and complexity. It enables teams to better leverage data across the board. By investing in software-as-a-service (SaaS) solutions, cloud infrastructure, and other modern, efficient data solutions, your company can be more agile with your data management strategies.

Instill a data-focused culture into all aspects of your company, including internal messaging. For an effective data culture to take hold, your enterprise must incorporate a strong data emphasis into an articulate data management strategy. This should apply to all employees across all levels of your organization, regardless of technical ability or role. Building this culture must start with (and then trickle down from) executive buy-in and be supported by training programs that help workers develop the skills and awareness needed to leverage data across all aspects of the business.

Establishing baseline, repeatable processes and methods to analyze data and a means to communicate valuable insights across departments will play a key role here. Focusing on data democratization and data literacy training for employees is also critical because the success of any data management strategy ultimately comes down to the people within your organization.

Incorporate machine learning (ML) and AI into data processes. Teams must embrace automation to enhance processes where they can. [Using ML and AI, IT teams can merge traditional data warehousing with modern data science techniques for more effective and streamlined data analytics, and thus, faster data-driven insights. For example, organizations can apply data science algorithms to optimize margins through dynamic pricing or personalize marketing campaigns throughout sophisticated customer segmentation.

Invest in hyperautomation. Along the same lines, hype around the concept of hyperautomation has been on the rise thanks to its benefits of reducing manual processes and advancing a company’s use of digital twins (virtual representations of systems and assets that can be used to test and optimize performance, increase productivity, and reduce risk). Defined by Gartner as “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible,” hyperautomation has great potential for improving an organization’s data-driven systems and strategies. For example, hyperautomation can help advance a company’s use of digital twins (virtual representations of systems and assets that can be used to test and optimize performance, increase productivity, and reduce risk). When applied in the healthcare industry, for instance, by running hospital-wide simulations, healthcare leaders can see the impact of staffing or ward layout changes, helping them better understand in advance whether a change in one department will create a bottleneck in another without having a physical impact on patients and workers.

Avoid vendor lock-in. When optimizing a data management strategy, it’s important for your organization to consider the flexibility to run analytics in different locations or on different services. Certain cloud vendors, for example, force businesses to stay in “their” cloud. The ability to run in multiple clouds and even on premises, or in a hybrid model where you get the best of both worlds, results in a more flexible IT strategy.

Final Thoughts

The next five years will bring even more change to your data management strategy. We’ll see growing complexities and costs, increasing pressures on data managers, CIOs, and chief data officers to improve their organization’s operating model, and a greater focus on data access and use as well as on extracting value from AI. Your business must take action now to establish a strong, impactful data management strategy that can withstand -- and even embrace -- these changes. The steps described here are a great place to start.

About the Author

Mathias Golombek CTO at Exasol, has been with the company since 2004. He initially was head of research and development. In 2014, he was appointed as a member of the executive board and chief technology officer. Mathias currently holds responsibility for the global product management, research and development (R&D), and technical customer support functions. Mathias studied computer science with a focus on databases, distributed systems, and development processes at the University of Applied Sciences in Wuerzburg, Germany (Diploma in Computer Science).

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