Data Management Strategy – The Only Guide You Need to Refer

Data makes the world go round, and organizations of all sizes are grappling with the daily challenge of organizing and processing massive volumes of information in a manner that best facilitates their progress.

Their ultimate goal is to make good data-driven decisions, but they lose their value without an effective way to organize all those metrics and statistics. A solid data management strategy helps companies harness the full potential of that information to achieve their goals.

This comprehensive guide will lay out the intricacies of handling and analyzing your data and direct you on the right path to maximize its usefulness. We’ll touch on the fundamental principles of data governance, quality, and security and provide practical techniques to help gain complete control over all data assets and optimize decision-making.

The Fundamentals of Data Management

Put simply, data management is the process of collecting, storing, analyzing, processing, and utilizing information gathered from various sources. The goal is to preserve the integrity and relevancy of that info for the duration of its lifecycle and make it easily accessible to every member of the organization. Implementing data management strategies in your backend operations avoids the creation of data silos and ensures every stakeholder can directly access the insights they need in real-time.

That’s why, fostering a data-centric culture in the workplace is imperative in addition to implementing policies and using tools. Having a team that understands the power of information and can effectively handle it from collection to consumption exponentially increases a company’s chance to succeed. Other key components and principles of data management include:

• Collection – To achieve the most comprehensive results, gather information from a vast array of sources. Identify the ones that fit your requirements, and be on the lookout for additional feeds that include relevant metrics and statistics. Vast amounts of information are generated daily, so this process is a continuous one.
• Integration – Data assembled from various places comes in disparate formats, making it difficult to identify the relevant facts. Unifying it into a singular type and configuration facilitates the extraction of the needed insights and standardizes the viewing experience.
• Quality – Having a big jumble of unorganized info isn’t very productive, so the next step is to polish it up. Ensure that the data is accurate, up-to-date, complete, and relevant. Remove duplicate entries, add missing values, and cross-check findings to ensure consistency across the board.
• Storage – The collected and processed data must be easily accessible to every organization branch. Implement an effective storage solution, such as a cloud-based warehouse, data lake, or centralized hub, with direct pipelines to user interfaces for seamless access and processing.
• Sharing – Fast data sharing throughout the organization encourages collaborative decision-making. When everyone has access to facts with the same quality, it reduces the occurrence of inner-department conflicts and ensures they are on the same page.
• Governance – Put in place policies and methodologies that preserve the integrity of any data that enters the system. They provide transparency and clear directions on how assets must be processed, stored, and handled and outline the access protocol, which builds a data lineage that is easy to follow.
• Security – Implement DPL strategies like encryption, access control, and audits to ensure safety and privacy and prove the firm has done its due diligence. The collected information must be protected from unauthorized modification, use, dissemination, or destruction.

Any business across multiple industries can enjoy the benefits of strategic data management. For instance, the retail giant Amazon manages its vast pools of information in a way that allows it to study consumer and employee preferences and behavior to increase user retention and optimize operational efficiency. Or consider the Mayo Clinic, which utilizes data management to improve and personalize patient care and develop new treatments.

Assessing Your Data Needs

The first step on your journey to developing an effective data management strategy is identifying and assessing the requirements of your organization. Those vary based on the industry you operate in, the size of your company, your business objectives, and the amount of data you take in and process.

To better recognize the specific needs of your company, try to answer the following questions:

What data is necessary to support the core of your business?
How can you glean a deeper understanding of your consumer base and their wants?
Which insights do individual departments need to make well-informed decisions?
How can you leverage the data you have to gain a competitive edge?

When you’ve pinpointed your organization’s data needs, it’s time to figure out how to fulfill them. The Internet makes it possible to access an unlimited number of channels that provide different statistics and metrics, so it’s important to filter out those relevant to your business. Identify your most used assets and streamline the acquisition process using specialized data migration tools.

Those sources can be external and pertain to consumer demographics, market research, competitor analysis, or internal, which reveal employee performance, financial growth, and client interactions.

But, the data that flows in is often unedited and unrefined, making it unsuitable for practical application. Performing regular audits paints a clear picture of the current data landscape in the company and ensures no critical data is overlooked. These checks reveal inconsistencies in the methods for collection, storage, and processing and make it easier to maintain a high-quality, current, and homogeneous database.

Designing a Customized Data Management Strategy

For any data management strategy to be effective, it must be tailor-made to meet your specific demand. It’s crucial that it aligns with the establishment’s objectives and provides an efficient way to store, organize, and protect its informational assets.

At the center of your strategy, you must position a tool that provides all the functions and options you’ll need to manage this process yourself. For example, Rivery provides easy-to-use data migration, transformation, integration, and security solutions, all packaged into one software. Such technology must be able to integrate into the existing system seamlessly yet still have adequate scaling capabilities that support future growth.

Next is devising a roadmap to showcase the important milestones on your road to improvement. A well-defined plan should include all foreseeable activities, assign responsibilities, and leave space for the necessary adjustments. It should include several phases, which allow for re-assessment and re-adjustment of the data management process.

Continuous improvements of this strategy also ensure your policies are always on par with industry standards and follow the best practices for data governance. While it may seem that data management and data governance can be used interchangeably, in essence, they are different concepts. To demonstrate the difference in meaning, we prepared the following table overview:

Ensuring Data Quality and Security

The increased handling of large data volumes within your company brings big benefits, as well as big responsibilities. You must ensure that every piece of information in your possession is complete, unique, accurate, current, and consistent across every platform. According to a recent survey, 30% of employees wasted their time performing menial tasks due to low-quality data. The use of data-proofing tools can save you time and increase productivity.

Another factor in maintaining high standards is implementing robust privacy and security policies and measures that protect from any external threats. To prevent even the smallest chance of a data breach, companies should consider investing in multi-layered security protocols, which include:

• Access controls – By instating user privileges and individual passwords, access to sensitive information is restricted to unauthorized persons within your organization.
• Network encryption – The data flow occurs within a secure virtual private cloud (VPC) and is secured by several types of encryption.
• Secure connectivity – Data is protected from “eavesdroppers” during transit with communication protocols like HTTPS and TLS.
• Data masking – Personally identifiable information is obfuscated during trial stages like product testing to prevent accidental exposure.

An important aspect of online data security is ensuring your policies follow the guidelines set in legislation such as the GDPR or the more US-centric CCPA. Organizations must carefully review and adhere to these regulations, obtain appropriate consent for data collection, process data transparently, and give individuals control over their personal information.

Building a Data-Centric Culture

Once you find the perfect tools to facilitate your strategic data management and harmonize your procedures with the best practices in data governance, it’s time to get employees on board. Building a data-centric culture requires fostering a mindset where data is valued, prioritized, and integrated into all aspects of decision-making.

Leadership should lead by example and emphasize how important current and accurate information is to the overall success of the company. But becoming a data expert doesn’t happen overnight. Provide your teams with training and materials centered around data literacy, security best practices, and the proper use of data tools. Many platforms, like Rivery, offer live demos and Q&A sessions, as well as webinars and e-books on this topic. Ensuring your employees have access to continuous learning programs will keep them updated on the latest trends and technologies in data management.

Or you could opt for the in-person option and bring in a data specialist to demonstrate the impact of a good data management strategy and cross-department collaboration in practice. For these initiatives to be successful, every team member must be willing to practice open communication and come together with other sectors to overcome any obstacles they encounter during any stage of the data management process.

When your employees understand that data is the central force that fuels an organization’s success, they’ll wholeheartedly embrace the new data-centric culture and use it to make informed decisions and achieve professional growth.

Overcoming Common Challenges

The road to achieving top-notch strategic data management is not always smooth. It’s important to recognize the hurdles in time and take the appropriate action to overcome them. The most common obstacles you might face during this process are:

• Data silos – This occurs when data is fragmented across multiple systems and becomes isolated and inaccessible to users in certain parts of the company. While that presents an inconvenience, it can be fixed by creating a single source of truth and using reverse ETL tools to extract information.
• Low data quality – Poor data quality can lead to inaccurate insights, misinformed decisions, and wasted resources. Automate the data handling process to eliminate human error, decrease duplications, and transform the data into a working format instantly.
• Data analysis – The scope of the insight gained from analyzing massive amounts of data can be overwhelming. Use dedicated software to visualize the gained metrics and statistics to support informed decision-making.
• Skill gaps – Not many companies have staff with the in-house skills to optimize their data architecture. Choose a software tool with pre-built data connectors and APIs requiring zero to minimal coding skills.

Measuring Success and Continuous Improvement

To see if your data management strategies are producing the desired results, you must establish key performance indicators (KPIs) that reflect the company’s objectives, such as data integration success rates or the number of security incidents. They must be specific, measurable, and quantifiable and demonstrate the achieved success or identify areas for improvement.

These KPIs must be communicated at every level of the internal hierarchy and serve as a starting point for a continuous feedback loop. Collecting feedback on performance with all stakeholders allows for a nuanced understanding of what is working well and where improvements are needed. This activity should be proactive, with mechanisms in place to regularly solicit input and adjust strategies accordingly.

It’s also important to celebrate the achieved milestones and successes. That showcases tangible results and actual success, motivating employees to continue making data-driven decisions.

Case Studies and Success Stories

Regardless of the size of the business or the industry in question, having a good data management strategy has become an essential operational tool, enabling organizations to gain relevant insights, make informed decisions, and achieve a competitive advantage. Platforms like Rivery offer solutions that play a crucial role in helping various companies overcome many data management challenges.

One notable example is the world leader in pharmaceuticals, Bayer, which needed to improve its data quality and increase data transparency. The company chose Rivery due to its fully-managed connectors and the possibility to custom-make more. Now, they are connected with 30% more sources and still manage to transform their data into a single workflow, making it easier to handle and transform.

Another successful case study is the American Cancer Society, which operates in over 5,000 communities and has helped 18 million cancer survivors so far. That is a lot of patient information to process, which is why it turned to Rivery to help it automate the data extraction process. Now, their data is orchestrated into a single pipeline from where it’s automatically ingested and executed in Snowflake.

The services Rivery offers have directly impacted the success of those companies by making them more efficient and organized. From accelerating data pipelines to enabling real-time analytics, these success stories prove the transformative power of effective data management.

Final Thoughts on Data Management Strategies

Devising a solid strategy for data management is a multifaceted undertaking that requires careful planning and the right tools. By effectively collecting, storing, organizing, and using their assets, businesses can make better data-based decisions and overcome the typical challenges that come with data management.

Use this ultimate guide as your ticket to fast-track success, and learn how to extract the most value from your data and transform it into workable insights that drive innovation, growth, and success.