Enterprise data is the linchpin of all business processes — companies around the world build their business strategies on insights derived from numerous, complex data points and big data analytics. Data management refers to end-to-end processes that include the sourcing, ingestion, storage and transformation of proprietary data for business intelligence (BI), reporting and analytics.

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Overview

In the 1960s, businesses would use punched cards and, later, mainframes to keep track of their data. The 1970s marked the introduction of the relational database management system (RDBMS), with data stored in neat, orderly rows and columns.

Fast forward to the 2000s, and the release of Hadoop and NoSQL systems expanded the scope of data management tremendously. All information, including semi-structured and unstructured data, could be utilized for business intelligence. Additionally, a disseminated computing model made processing large, complex data sets faster and more manageable. With IoT and streaming data added to the mix, software vendors are astutely adapting their data management and big data analytics software to capture and process such data for analysis.

Managing Your Data Today

A data management solution integrates data from disparate sources and blends and aggregates it into a structured format for data analysis and visualization. Companies often tend to hang on to all their data since “you never know when you might need it.”

Usually, data governance regulations mandate companies in certain industry domains, such as insurance and pharmaceuticals, to retain all their information over time; maintaining such enormous data sets can strain the company’s budget. The fact remains, though, that globally, 55% of data collected by companies remains unused. Referred to as dark data, it can cause businesses to miss out on business opportunities when they cannot find the information they need.

Effective data management is essential to surface key business metrics and, as such, serves as the core foundation of an entire data pipeline. Data management consists of multiple tools – extract, transform and load (ETL) tools, master data management (MDM) tools, database management systems (DBMSs), and reporting and analytics tools.

However, many enterprises prefer to go for integrated solutions that handle the complete data life cycle from ingestion to analysis. Some tools provide bare-bones data retrieval and reporting, while newer products integrate artificial intelligence (AI), engineering and cognitive sciences to drive business analytics.

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Primary Benefits

Data management gives you better control over your data, helping your brand stand out in the market and stay ahead of the competition. We discuss some of its benefits here.

Manage Your Big Data

Enterprises are literally drowning in their own data and finding themselves unable to harness it optimally. As a result, this data quite often wastes away unutilized, housed in siloed databases. Data management brings all this information together, giving companies a clearer view of existing and upcoming business trends. Regular data management tasks such as data ingestion, transformation, storage and backup can be automated now, freeing up IT resources for more important tasks.

Plan For the Future

With a clear picture of their data, companies can identify upcoming business opportunities and potential risks through what-if and scenario analysis and take timely action to maximize their ROI. Companies can see into the future with confidence in their data through predictive analytics – the holy grail for business planning. For instance, your company can make reliable data-backed decisions for the future by visualizing the impact of resource allocation on organizational spendings, such as payroll costs and operational expenses.

Data Quality Management (DQM)

Dirty data can cause mistakes to multiply rapidly, manifesting through messy, unreliable reports. Conversely, good quality data results in more meaningful insights. When organizations have access to data that is complete, consistent and accurate, they can make business plans for the future accordingly.

Efficient data management ensures excellent data quality, fostering trust in business data among employees and clients alike and inspiring confidence in analytics reports and insights.

Data Security

Since the economic crisis of 2008, privacy laws have gotten more stringent. The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010, GDPR and the California Consumer Privacy Act (CCPA) are some of the regulations that have gone into effect since then. Ensuring compliance with newer privacy laws is a challenge for many organizations, especially with data siloed across disparate databases and the fact that these regulations change often.

Modern data management tools take care of these concerns and provide up-to-date compliance with the latest laws through regular updates. Enterprises can conveniently make their databases available for compliance checks and data quality analyses. To go the extra mile, they can conduct scenario simulations in-house and check the impact of implementing internal and external regulatory compliance on their data.

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Global data creation and replication is expected to show a CAGR of 23% over the 2020-2025 forecast period, according to the IDC. As data takes a larger role in our lives, businesses will be paying closer attention to their data management strategies, streamlining business processes and investing in scalable, flexible, and reliable solutions.

Effective data management can help them utilize their data that’s gone dark and give them a competitive advantage in the market, which means that more and more companies are now looking for the best data management tools.

Who Uses Data Management?

Enterprises across domains are jumping onto the data management bandwagon. Let’s take a look at who some of them are.

• Sports clubs leverage data management to derive performance insights that help them play better. Sentiment analysis of social media posts helps them improve engagement with fans, increasing their popularity.
• Life sciences companies manage clinical trials with data management tools for records tracking and data anonymization to maintain patient confidentiality.
• Retail, e-commerce, hospitality, aviation and consumer goods manufacturers and vendors require real-time customer activity data to sync their offerings with market demand and cater to customers more effectively.
• Legal firms and private individuals can access court records by case and research information on judges, lawyers and other law firms with legal data management software like UniCourt.
• Healthcare institutions use master data management for EMR and EHR, as well as operationalizing long-term care protocols and managing research projects.
• Financial institutions such as banks and insurance companies use data management solutions to offer users secure online access to their data, prevent fraud and manage risk.
• Public service organizations, such as nonprofits and government agencies, need data to plan economic development programs and streamline resource allocation, including staffing and funds.

Businesses in telecom, media and tech, manufacturing, agriculture and many more industries are leveraging these solutions to boost productivity and maximize their ROI. With benefits such as the ability to manage all their data and improve decision-making, enterprises across the spectrum are jumping on the data management bandwagon in a big way.

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Best Practices

Enterprises can mitigate data management challenges by integrating best practices into their data strategy; we’ll discuss some data management best practices that you can implement in your business.

Reduce Dark Data

Reducing dark data needs a proactive enterprise approach. Identify the role that data plays in your organization, strictly capturing only the information you will need and mapping it to a data catalog for reference. Control your data better: define a data retention and disposal strategy and ensure consistent compliance by appointing a data guardian.

Maintain A Dedicated Team

Data management involves focused efforts, and many business owners just can’t afford to spare the time, even if they are qualified data management professionals. A dedicated data management team can take the burden off your hands, and even though hiring data engineers and architects might cost you a tidy sum initially, the dividends in terms of better-streamlined operations can more than make up for the cost incurred.

Facilitate Data Discovery

Creating a data layer on top of existing data enables easy data exploration and discovery. A standard query language layer will allow all your power users and stakeholders to access desired data insights quickly. For easy access to historical and current data, more and more organizations opt to build a data catalog with metadata mapping. Maintain a central catalog of all data assets on-premises and in your organization’s multi-cloud environments, backed by native connectors and REST-based APIs to scan and extract metadata from all data assets.

Invest in Managing Your Data

Though many enterprises across the globe recognize the value of effective data management in theory, the reality is that these technologies exist largely in silos and are often sidelined when it comes to including them in the corporate culture. According to Dan Power, Managing Director, Data Governance, State Street Global Markets:

“The primary challenges are in organizational culture and management commitment. Technology questions are actually relatively easy to overcome. But changing the company’s culture to be more data driven, and having the will to spend human and financial resources are more challenging. Most senior executives think ‘data’ is an IT responsibility. But the reality is that it requires a business / IT partnership, and a lot of large corporations don’t have a strong track record of business & IT collaboration and alignment. Data spending has typically been lumped in with application development investments, and after the IT projects are over, the company doesn’t think about spending money on data again until there is some type of crisis.”

Making these technologies work for the organization requires proactive involvement from high-level stakeholders. Team managers can act as effective facilitators to ensure that data management does not fall through the cracks and becomes the foundation for all of the company’s data analytics processes.

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Notable Trends

Innovative data storage solutions, automation and ubiquitous implementation models have spawned quite a few exciting trends in data management. According to Dan:

“There are several that I am tracking: data ethics – the responsible and ethical use of data in artificial intelligence and machine learning; technology investments – selecting and implementing next-generation data governance platforms, including data lineage discovery, metadata catalogs, and data quality.”

Let’s take a closer look at some of these trends.

Data Ethics

Legal compliance with data protection laws does not mitigate all the risks associated with data privacy. Beyond defined privacy regulations, there is an ethical aspect to data that applies even when people have consented to organizations using their personal information.

Specific details such as gender, race, religion, economic status and sexual orientation can create conscious and unconscious bias in humans and data training models, causing more harm than good. Deserving individuals can be deprived of life-changing opportunities, such as college admissions, quality healthcare, federal benefit schemes and jobs.

When used to sway public opinion and manipulate political narratives, personal data can become a dangerous weapon for self-serving entities with vested interests. Data privacy regulations such as GDPR intend to give users total control over and transparency into how enterprises use their data. With data scientists designing automated decision-making systems that will need minimal human intervention, data ethics will be a critical aspect of application development.

Data Lineage Tracking

Users want to know where the data they consume comes from; if they are unsure of its lineage, they will not trust the reports and analyses that include this data. Unreliable data can result in low adoption of BI products and technologies, which further emphasizes the need for data quality management at the enterprise level. If users aren’t satisfied with the quality of existing data, they might try to create their own data sets, resulting in rogue data marts that constitute low-quality and often contradicting data silos.

Data lineage should ideally record the journey of data from its source, transformation history, quality, usage, and metadata. Automated data lineage takes this task off the plate of enterprise IT teams, freeing them up to focus on other tasks and ensuring information quality at the same time. Organizations across the globe are acutely aware of the need to invest in data quality management to help ensure accuracy and boost business, a trend that is likely to continue.

Application Consolidation

Often, the software tech stack in organizations includes multiple tools providing the same functionality. Application proliferation — a surplus of software without significant enhancement in functionality — is a drain on enterprise resources in terms of additional licensing and subscription fees, and IT support.

The solution?

Application consolidation: it involves bringing together multiple instances of the same software or migrating consumers of two separate applications to one. Instead of distributing data across various systems, application consolidation ensures that data is more streamlined and of better quality.

As Dan Power puts it:

“Large firms can no longer afford to build or buy a new application for every separate function, they need to do more with less, to integrate and automate more, and to reduce the footprint of the larger, older, more expensive applications that were acquired or built more than 10 years ago.”

Application consolidation reduces the number of software contracts and vendors you need to deal with, saving on licensing costs. Your team can be more productive since they don’t need to learn how to use different solutions, and the learning curve is likely to be less steep. You will need fewer IT staff, servers and operating systems. Enterprises are now realizing the manifold benefits of this approach and investing in consolidating their tech stack.

Natural Language Processing (NLP)

Organizations now have more unstructured data than before, including emails, chat transcripts, complaint logs, outbound marketing materials, legal documents and more. To mine this data for regulatory compliance and sentiment analysis, teams would need to go through each chat transcript, email and document for insights.

Enterprises can now mine data faster with ML-backed natural language processing (NLP) and save on resources and time. Companies can discover insights from virtually any kind of data — structured, semistructured and unstructured — thanks to NLP, semantics and knowledge graphs created via machine learning. A knowledge graph is a semantic network of real-world objects, events, situations and the relationships between them, visualized as a graph.

Through NLP, users can easily interpret visual information and ask the right questions to get answers that help them optimize business opportunities. Pharma companies use knowledge graphs for R&D and drug discovery, while banks leverage them to streamline their operational processes and personalize customer experiences. NLP enables faster time to insight with self-service data interpretation and analysis, which is and will continue to be incredibly valuable in today’s fast-paced digital era.

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In Summary

The evolution of data volumes, structures, interfaces and latencies is changing the information management landscape.

The emergence of new technologies like the cloud, NoSQL and knowledge graphs is reshaping data analytics and development paradigms. Innovative technologies like serverless computing speed up data processing tremendously, with zero provisioning and capacity planning required. Tools with machine learning and artificial intelligence capabilities have been game-changers in helping automate data management workflows.

With these advancements and more to come, data management is key to success.

How have you leveraged effective data management? What other challenges have you faced while managing your data? Share your thoughts in the comments below.

Contributing Thought Leaders

Dan Power is a data management expert with more than twenty years of experience in enterprise technology, management consulting and strategic alliances. He’s written more than 35 articles and white papers on managing business data. Dan was the principal consultant for Hub Designs where Apple, AIG, LPL Financial, CIT Group, Marsh & McLennan, Dun & Bradstreet, LexisNexis, Red Hat, EMC, and Harvard Pilgrim Health Care were among his many clients. Having worked with Deloitte, and CSC earlier, Dan is presently the Managing Director and Head of Data Governance at State Street Global Markets.