Data management comprises the processes, systems and solutions that are used to acquire, use, improve, protect and process valuable data assets. Successful data management strategies focus on achieving these key objectives with the greatest speed and efficiency and at the lowest possible cost. Because of this, automation is quickly becoming one of the most important features in any data solution. Organizations need to convert raw data into meaningful insights as quickly as possible to maximize revenue and compete in today’s cutthroat marketplace.

With the evolution of data solutions and strategies, there has been a growing focus on empowering business users and enabling self-service options for data tools. However, the future doesn’t lie in “do-it-yourself” data tools, but in data software that does all of the heavy lifting. Eliminating manual processes enhances operational efficiencies, lowers costs and minimizes the risks of data errors due to human intervention.

For years, companies have sought innovative ways to automate data management processes and procedures to improve performance and grow their bottom line. Still, businesses face a variety of challenges in three crucial domains of data management: data prep and analytics, data governance and data quality.

Improving Data Prep and Analytics Processes

Organizations are continually challenged by the process of data preparation (finding, combining, cleaning and transforming raw data into curated assets) and data analysis. Despite the need for fast and accurate insights, data prep has traditionally been a manual, time-consuming effort relegated to IT resources. As with any manual process, errors can proliferate. In addition, when business users must rely on IT to manage, prepare and analyze large volumes of data (because they are the only department equipped to utilize tools that require knowledge of technical languages like SQL, Java and Python), requests quickly back up and delay timely insights.

Data prep tools have evolved into self-service data prep, empowering more users to engage in data analysis. Although, the next step in that evolution is delivering new levels of automation to the process, eliminating the risks associated with human intervention, speeding the process of data prep and analysis and enabling faster delivery of meaningful business intelligence. As the volume and variety of data continues to grow, manual methods simply can’t keep pace. Automated processes ensure fast results even with the largest of data loads, employing techniques such as machine learning to ensure effective and efficient analysis of data at scale.

Fostering Enterprise-Wide Collaboration Through Data Governance

Organizations across key industry segments can benefit from analytics. However, arduous, manual data governance processes are often a barrier to producing meaningful insights from data analysis. Instead, companies need to eliminate spreadsheets and wikis as governance tools and replace them with a solution that provides easy access and visibility into the available data, its owner/steward, lineage and usage, as well as its definitions and business attributes. It should provide automated workflows for updating assets and encourage collaboration and knowledge sharing across the entire enterprise. A culture of open communication between the IT department and various lines of business builds consensus on data meaning and usage and eliminates confusion as business users analyze data.

Data governance also ensures that data is used appropriately and effectively by assigning data ownership for individual assets to establish accountability. Establishing processes and policies for data access and usage also assures that information is readily available and appropriately accessed.

With full participation across the enterprise, data governance provides complete transparency into an organization’s data supply chain. Business users are empowered to quickly derive meaningful insights—provided they also have high-quality data.

Automating the Data Quality Process

Establishing high-integrity data is a challenge because internal data is continually being created and transformed. Data ingested from external sources adds another layer of complexity, as it introduces data of unknown quality into the enterprise. Whatever the source of data, its integrity is always at risk. As data travels through the data supply chain, it’s exposed to new systems, processes and uses. Organizations must solve data quality issues before they proliferate and result in flawed analytic outcomes. As information environments become increasingly large or more complex, disparate databases, applications, systems and other data sources make it increasingly difficult to identify and solve ongoing data integrity issues.

To ensure enterprise data quality, organizations can automatically monitor, improve, measure and score data quality within a data governance framework to prevent data issues from ever occurring. This also enables business users and other data consumers to easily determine what data is best for critical analytics. By layering in controls for parsing, standardization, cleansing, profiling and monitoring and incorporating machine learning algorithms, organizations can automate data quality tasks to improve overall data integrity. Employing machine learning capabilities alongside traditional controls results in continuous data quality improvement to produce accurate, consistent data for use and analysis.

When businesses improve critical data management processes, they quickly deliver well-curated, easily understandable and high-quality data. Business users can effortlessly determine what data is best for meaningful analytics while developing valuable intelligence.

About the Author

Tim Segall, Chief Technology Officer at Infogix

Tim joined Infogix in 2018 with the acquisition of Lavastorm, where he served as Chief Executive Officer. Today, as Infogix’s first Chief Technology Officer, Tim brings to the organization extensive expertise and a proven track record of setting strategy and overseeing the full lifecycle of product development through delivery. Prior to Lavastorm, Tim held C-level positions at Zaius, Inc., Genesys/SoundBite Communications, and ManageSoft. He holds a bachelor’s in computer science from the University of Queensland, Australia.