Most modern businesses recognize the value of data, and for small businesses, this often means relying on reports generated within the individual software platforms they use for daily operations. However, there comes a time when unifying this data in a central, standardized source is desirable. To effectively organize and secure this data requires a process known as data management.
What is data management?
Data management is the process by which businesses gather, store, access and secure data from various business software solutions. Employing data management enables more efficient access to data analytics that offer insights that are needed to improve business operations and identify opportunities for improvement. By establishing a better framework to access the wide swaths of data that every business generates, companies can make more informed decisions and improve their ability to deliver valuable products and services to their customers.
"Data management involves multiple disparate functions and systems working together to move, organize, and secure data such that it is accurate, precise, accessible and protected," said Christopher Risher, senior program manager of application management services at Onepath.
In a modern business environment, virtually every piece of software collects data. These include accounting software, customer relationship management (CRM) software, point of sale software, credit card processing software and more. These systems feed a wide variety of data into the business, including customer data, financial data and more.
"Nowadays every company has data, from the multinational giants of IT to the small local breweries. Some data are sensitive, some are history, some can be used for future predictions, some for auditing, and so on," said Rosaria Silipo, principal data scientist at KNIME. "With so much data and so many different properties and usages, a different set of rules and competences is required to handle each subset of data. You can see then that data management can quickly become a quite complex and tricky task, which can bring further prosperity or further problems to the company."
As the number of business software platforms proliferate, so too does a business's ability to gather data and employ data analytics to derive key insights from it. However, organizing that data in a centralized system can sometimes be challenging. Developing a data management strategy is a must for businesses that want to maintain a competitive advantage and improve both customer-facing and internal elements of business operations.
How does data management work?
To begin implementing a data management policy, businesses need to understand the tools available to them to do so.
"Managing data typically begins with a project that’ll get started in one of the knowledge areas and iterate through the other knowledge areas," Risher said. "Utilizing cloud-enabled tools can assist in the rapid development of a data management platform. These cloud tools can empower an organization regardless of the location of their data."
In addition, data management should serve to standardized data in a way that makes it effective for business purposes. Not every software platform will collect data in the same way, or even collect the same types of data. Data management serves to unify these data silos so they become useful when combined.
"Organizations are dealing with more data from more sources than ever before (known as big data). They have come to realize that all this data can provide a wealth of new insights into customer buying behavior and the dynamics of their industry – but only if this data is managed and trusted," said Todd Wright, head of data management solutions at SAS.
How data is managed directly relates to data quality, which must be unassailable if any data analytics efforts are to bear fruit. Decisions made based on faulty data will in turn be faulty decisions, so data quality should be of the utmost importance to any business relying on this information.
Types of data management systems
Data management systems make the process of data management more manageable, automating some of the most arduous aspects of unifying and reviewing key data. These systems incorporate databases and analytics tools that allow businesses to not only store and organize important data but also query the system as needed. The best systems consolidate data into useful reports that include visualizations that provide the ability to contextualize data at a glance. Some even incorporate automated decision-making recommendations empowered by machine learning, helping key stakeholders make more informed, effective choices about how to govern the business's operations.
Some examples of data management systems include:
- Data governance: Tools like Informatica, Azure Data Catalog, and Talend improve a business's ability to track data and associate it with metadata for later retrieval. Metadata helps improve data structure, organizing it in such a way that makes it more useful. Data tracking tools help businesses understand each data asset they have at their disposal. Together, these elements are critical for making vast databases truly useful. Data governance is all about how data is organized, stored and secured, Risher said. Through data governance, businesses can guarantee data quality.
- Business intelligence: BI tools like Microsoft Power BI, Azure Synapse Analytics, Tableau and Snowflake serve to improve data storage and security while also availing it to decision-makers in a structured, contextualized way. BI tools are essential for making use of vast databases, which no human could ever hope to manually sort through to derive meaningful insights.
- Data integration: Tools like Azure Data Factory, Logic Apps and Functions offer user-friendly interfaces for centralizing disparate sources of data, which can yield new insights. For example, data from accounting software and a CRM might seem separate and unrelated until organized together. Taken together, though, the data from these disparate systems might help paint a more complete picture of business cash flow and revenue. This is true for all sources of seemingly separate, but in actuality, related data.
"The goal of data management is to give an organization reliable and quickly accessible data through which decisive action can be taken in a secure manner," Risher said.
These data management systems are crucial tools to achieve that goal, especially as the amount of data collected by businesses becomes too vast for any human to contextualize manually. Data management systems are required to make sense of the overwhelming amount of data most businesses generate.
How to develop a data management strategy
When developing a data management strategy, you should start by understanding your key business objectives. Make a list of these objectives and then identify what data you are already collecting that is relevant to each objective, noting any overlap between objectives or gaps in the data you already maintain. Once you have established a comprehensive list, ask yourself what is the best way in which to organize and secure this data for later retrieval.
"From a strategy perspective, data management and its underlying knowledge areas provide a construct to give analytical data models what is needed to receive reliable insights," Risher said. "Without proper implementation of data management controls, some level of the pipeline that feeds an analytical data model can be rendered unreliable. If we are basing strategic, forward-thinking decisions off poorly gathered data, then we are likely impacting the business by making incorrect decisions."
To better structure the vast troves of data generated by a company on any given day, it is important to tie that data to your specific business objectives. Not only will these objectives guide the collection and organization of data, it also makes clear who should be able to access that data, and when and why.
"When we take ownership of the data in the company, we need to make sure we understand their position in the bigger strategy. Based on their role and features, we need to define a sub-strategy for protection, storage, and usage," Silipo said. "A successful data management strategy allocates a place, a task, and a policy to each subset of the data, in terms of privacy, storage, and usage."
A data management strategy must incorporate multiple goals, including the ability to audit business operations, monitor progress toward organizational goals, and gain insight into what is working and what is not. Each of these goals requires a different approach to data management, meaning your systems and policies must be flexible enough to address each while being standardized enough to offer suitable levels of security and restrict access to key stakeholders who need to access the data most.
"Depending on the goal, you need a different tool to manage a different subset of data: from classic databases to highly secure data repositories, from data visualization tools to more advanced data analysis tools," Silipo said.
Data management best practices
These data management best practices can improve your organization's relationship with the data it collects and stores, making it easily accessible for use in improving business processes, as well as ensuring collection and usage comply with laws and regulations and up to current security standards.
Maintain a team with data management skills.
Data management is a vast and complex area of business operations. That means it requires a knowledgeable and dedicated team of data management professionals to manage it correctly. Few small business owners are capable of giving data management processes the time and attention they deserve, even if they have a background in data themselves. Hiring a data management professional team with the depth and breadth of knowledge to do so is an absolute must.
"First of all, you need to have a global vision of the data strategy in your company, to make sure that all pieces of information are recorded and all are exploited to generate more insights into the company process and business," Silipo said.
An experienced and skilled team with advanced data management skills is crucial to developing and refining a global approach to data management. Rely on your team, whether in-house or outsourced, to act not just as managers but consultants when it comes to connecting your business's data to its wider operations.
This team should be equipped with user-friendly tools to monitor, access, and organize data, both while it is stored and as it is collected. Employing some of the data management systems mentioned above should be a top priority alongside hiring the right team.
Establish a data compliance plan.
As data privacy laws become increasingly common – such as the EU's GDPR or California's CCPA – data privacy compliance is critical. Not only should your data management plan be useful for your business operations, it must also be auditable in a way that easily demonstrates compliance to regulators and business partners.
"You need to know how each subset of data must be protected, stored, and analyzed depending on its nature and on its strategic importance," Silipo said. "Here a number of skills are joined together: legal skills to design the rules, IT skills to see the implementation of the rules, programming skills to retrieve the data, and some statistics and data analysis to understand how these data can become useful."
Regularly review data security policies.
As any cybersecurity professional will tell you, the threat landscape is constantly changing, and malicious actors are becoming more sophisticated in the way they infiltrate systems. Small businesses are prime targets, because hackers know they are typically less protected than large enterprises. As a result, small business owners must regularly revisit their security policies and revise them to meet the threats of the day. This includes giving your IT team the ability to monitor for and respond to new threats as they emerge, taking a proactive approach to the protection of your centralized data.
"Security is always top of mind. So, having access to a security professional, such as a CISO to validate the security parameters is extremely valuable," Risher said.