What is data management?

Too often, businesses are making key decisions based on data they can’t really see or understand.That can compromise business intelligence — which is the key to retaining a competitive edge in any data-driven industry. To combat this issue, companies must take an active role in managing and protecting their data throughout its lifecycle. Does your organization have the data management plan it needs to thrive in the global marketplace?

What is data management?

Data management refers to the professional practice of constructing and maintaining a framework for ingesting, storing, mining, and archiving the data integral to a modern business. Data management is the spine that connects all segments of the information lifecycle.

Data management works symbiotically with process management, ensuring that the actions teams take are informed by the cleanest, most current data available — which in today’s world means tracking changes and trends in real-time. Below is a deeper look at the practice, its benefits and challenges, and best practices for helping your organization get the most out of its business intelligence.

7 types of data management

Data management experts generally focus on specialties within the field. These specialties can fall under one or more of the following areas:

  1. Master data management: Master data management (MDM) is the process of ensuring the organization is always working with — and making business decisions based on — a single version of current, reliable information. Ingesting data from all of your data sources and presenting it as one constant, reliable source, as well as repropagating data into different systems, requires the right tools.
  2. Data stewardship: A data steward does not develop information management policies but rather deploys and enforces them across the enterprise. As the name implies, a data steward stands watch over enterprise data collection and movement policies, ensuring practices are implemented and rules are enforced.
  3. Data quality management: If a data steward is a kind of digital sheriff, a data quality manager might be thought of as his court clerk. Quality management is responsible for combing through collected data for underlying problems like duplicate records, inconsistent versions, and more. Data quality managers support the defined data management system.
  4. Data security: One of the most important aspects of data management today is security. Though emergent practices like DevSecOps incorporate security considerations at every level of application development and data exchange, security specialists are still tasked with encryption management, preventing unauthorized access, guarding against accidental movement or deletion, and other frontline concerns.
  5. Data governance: Data governance sets the law for an enterprise’s state of information. A data governance framework is like a constitution that clearly outlines policies for the intake, flow, and protection of institutional information. Data governors oversee their network of stewards, quality management professionals, security teams, and other people and data management processes in pursuit of a governance policy that serves a master data management approach.
  6. Big data management: Big data is the catch-all term used to describe gathering, analyzing, and using massive amounts of digital information to improve operations. In broad terms, this area of data management specializes in intake, integrity, and storage of the tide of raw data that other management teams use to improve operations and security or inform business intelligence.
  7. Data warehousing: Information is the building block of modern business. The sheer volume of information presents an obvious challenge: What do we do with all these blocks? Data warehouse management provides and oversees the physical and/or cloud-based infrastructure used to aggregate raw data and analyze it in-depth to produce business insights.

The unique needs of any organization practicing data management may require a blend of some or all of these approaches. Familiarity with management areas provides data managers with the background they need to build solutions customized for their environments.

Benefits of data management systems

Data management processes help organizations identify and resolve internal pain points to deliver a better customer experience.

First, data management provides businesses with a way of measuring the amount of data in play. A myriad of interactions occur in the background of any business — between network infrastructure, software applications, APIs, security protocols, and much more — and each presents a potential glitch (or time bomb) to operations if something goes wrong. Data management gives managers a big-picture look at business processes, which helps with both perspective and planning.

Once data is under management, it can be mined for informational gold: business intelligence. This helps business users across the organization in a variety of ways, including the following:

  • Smart advertising that targets customers according to their interests and interactions
  • Holistic security that safeguards critical information
  • Alignment with relevant compliance standards, saving time and money
  • Machine learning that grows more environmentally aware over time, powering automatic and continuous improvement
  • Reduced operating expenses by restricting use to only the necessary storage and compute power required for optimal performance

Consumers and buyers benefit from good data management, too. By learning their preferences and shopping habits, businesses can offer customers faster access to information they want. Customers and prospects can enjoy customized shopping experiences and trust that personal and payment information is used and stored with respect for data privacy, making purchases simple.

Today, top retailers like Tape à l’oeil rely on data management to design customer experiences that measure omnichannel shopping and buying behaviors, satisfying customer demand in almost real-time. All of that is powered by good data management.

Data management challenges

All these benefits don’t come without climbing some hills. The ever-growing, rolling landscape of information technology is constantly changing and data managers will encounter plenty of challenges along the way.

There are four key data management challenges to anticipate:

  1. The amount of data can be (at least temporarily) overwhelming. It’s hard to overstate the volume of data that must come under management in a modern business, so, when developing systems and processes, be ready to think big. Really big. Specialized third-party services and apps for integrating big data or providing it as a platform are crucial allies.
  2. Many organizations silo data. The development team may work from one data set, the sales team from another, operations from another, and so on. A modern data management system relies on access to all this information to develop modern business intelligence. Real-time data platform services help stream and share clean information between teams from a single, trusted source.
  3. The journey from unstructured data to structured data can be steep. Data often pours into organizations in an unstructured way. Before it can be used to generate business intelligence, data preparation has to happen: Data must be organized, de-duplicated, and otherwise cleaned. Data managers often rely on third-party partnerships to assist with these processes, using tools designed for on-premises, cloud, or hybrid environments.
  4. Managing the culture is essential to managing data. All of the processes and systems in the world won’t do you much good if people don’t know how — and perhaps just as importantly, why — to use them. By making team members aware of the benefits of data management (and the potential pitfalls of ignoring it) and fostering the skills of using data correctly, managers engage team members as essential pieces of the information process.

These and other challenges stand between the old way of doing business and initiatives that harness the power of data for business intelligence. But with proper planning, practices, and partners, technologies like accelerated machine learning can turn pinch points into gateways for deeper business insights and better customer experience.

3 data management best practices

Though specific data needs are unique to every organization’s data strategy and data systems, preparing a framework will smooth the path to easier, more effective data management solutions. Best practices like the three below are key to a successful strategy.

1. Make a plan

Develop and write a data management plan (DMP). This document charts estimated data usage, accessibility guidelines, archiving approaches, ownership, and more. A DMP serves as both a reference and a living record and will be revised as circumstances change.

Additionally, DMPs present the organization’s overarching strategy for data management to investors, auditors, and other involved parties — which is an important insight into a company’s preparedness for the rigors of the modern market.

The best DMPs define granular details, including:

  • Preferred file formats
  • Naming conventions
  • Access parameters for various stakeholders
  • Backup and archiving processes
  • Defined partners and the terms and services they provide
  • Thorough documentation

There are online services that can help create DMPs by providing step-by-step guidance to creating plans from templates.

2. Store your data

Among the granular details mentioned above, a solid data storage approach is central to good data management. It begins by determining if your storage needs best suit a data warehouse or a data lake (or both), and whether the company’s data belongs on-premises or in the cloud.

Then outline a consistent, and consistently enforced, agreement for naming files, folders, directories, users, and more. This is a foundational piece of data management, as these parameters will determine how to store all future data, and inconsistencies will result in errors and incomplete intelligence.

  • Security and backups. Insecure data is dangerous, so security must be considered at every layer. Some organizations come under special regulatory burdens like HIPAA, CIPA, GDPR, and others, which add additional security requirements like periodic audits. When security fails, the backup plan can be the difference between business life and death. Traditional models called for three copies of all important data: the original, the locally stored copy, and a remote copy. But emerging cloud models include decentralized data duplication, with even more backup options available at an increasingly affordable cost for storage and transfer.
  • Documentation is key. If it’s important, document it. If the entire team splits the lottery and runs off to Jamaica, thorough, readable documentation outlining security and backup procedures will give the next team a fighting chance to pick up where they left off. Without it, knowledge resides exclusively with holders who may or may not be part of a long-term data management approach.

Data storage needs to be able to change as fast as the technology demands, so any approach should be flexible and have a reasonable archiving approach to keep costs manageable.

3. Share your data

After all the plans are laid for storing, securing, and documenting your data, you should begin the process of sharing it with the appropriate people.

Here are some critical questions to answer before other people access potentially critical information:

  • Who owns the data?
  • Can it be copied?
  • Has everyone contributing to the data consented to share it with others?
  • Who can access it and at what times?
  • Are there copyrights, corporate secrets, proprietary intellectual property, or other off-limits information in the data set?
  • What else does the organization’s data reveal about itself?

With those and other questions answered, it’s time to find a place and means of sharing the data. Once called a repository, this role is increasingly filled by software and infrastructure as service models that are fine-tuned for big data management.

Data management strategy for software-as-a-service (SaaS)

Industry-leading data management and integration platforms like Talend’s provide a unified way of moving and managing all data operations, from code building to cold archive storage. By visualizing complex coding tasks, working from user-friendly templates, managing compliance considerations, and more, data management software speeds and simplifies the complicated, and brings an organization’s entire data picture to light on a single pane of glass.

With 24/7 uptime, industry-leading reliability, and a simpler learning curve, services like Talend’s give data managers greater control for less expense — in time and real dollars — than approaches built entirely in-house.

Getting started with data management software

Big data has implications for businesses in almost every industry. As it continues to become more and more important for real-time decision making — keeping organizations competitive and customers engaged — managing all that data becomes more and more important as well.

Proper data management is a vital step toward better overall data health and ensuring that you are getting the most value out of your data. To learn more about how Talend can help with your big data management challenges and start delivering critical business intelligence, check out Talend’s suite of data management tools.

Ready to get started with Talend?