Data Management Goals and Challenges




Wissen Team


August 16, 2023

Efforts toward digital transformation are in full swing. From implementing new systems to modernizing legacy ones, there's a lot of work being done to improve business performance, boost agility, and increase security. But no transformation can be successful without effective data management.

As businesses look to turn data into valuable insights, they find themselves submerged in a sea of data. Having tons of data is certainly good for driving the right BI and analytics outcomes. The problem begins when this data needs to be efficiently, securely, and continuously managed.

The Goals of Data Management

Data has become the new fuel powering modern organizations, empowering them to unearth valuable insights into their operations, supply chains, customers, and future business prospects. To power business decisions, the growing volume of data must be properly collected, cleansed, stored, managed, and shared, and that’s where data management comes in.

Data management enables organizations to make the most of clean, updated, and relevant data. This way, data can be analyzed for business decisions. It's noteworthy that as organizations worldwide create and consume data at unprecedented rates, data management empowers them to swiftly find the information they need for analysis and make sense of the vast quantities of data.

Data management acts as the primary step to employing effective data analysis at scale. This leads to important insights that further add value to customers and improve the bottom line. With effective data management in place, people across the organization can find and access trusted data, get visibility on processes across the business, and take appropriate action.

So, what are the goals of data management?

  • To ensure individuals, businesses, and related entities have seamless access to updated, reliable, and consistent data they need for accurate and timely decision-making
  • To cleanse and update constantly generated data and ensure it complies with necessary data security and governance standards
  • To protect data from loss, theft, misuse, and breach by constantly implementing and updating authentication and encryption
  • To continuously back up data and ensure it is easily retrievable in the event of an outage or downtime
  • To safeguard personal information to stay compliant with consumer protection laws
  • To effectively scale data with repeatable processes and keep data and metadata up to date
  • To minimize potential errors by establishing repeatable processes and policies for usage and ensure seamless scalability

The Challenges

Despite the significance of data management in today’s age of data overload, it is not easy to achieve. Enterprise data comes from different sources such as apps, smart devices, sensors, social media interactions, and so on, most of which are not compliant with regulatory standards. With so many ways and means that data can be collected within an enterprise, problems with data management are rife. Storing massive amounts of data, cleaning and preparing it for analysis, and integrating it for further use is a complex task.

Let’s understand the top challenges:

  • The sheer volume of data being generated every single day makes it difficult (if not impossible) to efficiently organize data and establish appropriate processes for using that data.
  • The presence of multiple siloed databases makes it difficult for organizations to ensure data flows seamlessly across systems, teams, and locations.
  • The lack of the right processes causes several problems in overcoming the issue of inconsistent, incorrect, and incomplete data sets across departments.
  • Inadequate data management and standardization across the enterprise causes several integration issues across databases.
  • Data residing in different enterprise-level applications impacts the quality and timeliness of data-driven decision-making.
  • Lack of skilled data management specialists makes storing and accessing massive amounts of data while ensuring the security, reliability, and uptime of databases challenging.
  • Complex data tools impact quick and easy access of data to those who need it, when they need it, and where they need it.

The Solution

If you want to overcome the challenges of data management, there are several steps you can take. For instance, you can:

  • Invest in modern data management software that automates key tasks and ensures the data your employees use is constantly cleaned and updated.
  • Make use of new-age databases that are designed for high-scale, high-performance data management.
  • Automate data governance tasks to get insights more quickly, minimize data quality issues, and avoid non-compliance. 
  • Embrace frameworks like Apache Hadoop to enable distributed processing of large data sets — across clusters of IT systems and devices.
  • Adopt innovative ETL tools to streamline the process of extracting, scrubbing, and consolidating data for quality and consistency.
  • Embrace context-aware analytics models to understand relationships between data points and enable deeper analysis. 
  • Leverage low-code/no-code tools to speed up the data management process and enable everyone in your organization to easily work with complex data sets.
  • Implement composable data and analytics to effectively combine and consume analytics capabilities from various data sources across the enterprise and pave the way for intelligent and faster decision-making.
  • Extend data management tasks to edge environments to reduce latency and enable better visibility via active metadata. 
  • Improve data literacy throughout the organizations and increase self-service access to analytics tools and data via effective data democratization. 
  • Engage with a qualified data management expert who can help you establish the right data quality policies and enable the right data governance measures.
  • Build a data mesh architecture to decentralize the management of different data sources and enable teams to perform complex data analysis on their own. 
  • Enable intelligent metadata management using AI/ML and create a common foundation across all data management processes. 
  • Integrate metadata management into your cloud processes to drive agile, data-driven decision-making at scale. 
  • Invest in augmented data management to improve the quality of master data management processes and reduce costs. 
  • Exploit the features of metadata-driven data fabric to get alerts and recommended actions for people and systems through continuous listening and learning. 
  • Leverage the capabilities of AI tools like ChatGPT to combine columns, summarize records, convert formats, and perform complex calculations using plain English. 
  • Enable effective and continuous data observability to have better visibility into the health of data across systems and minimize data quality or system downtime issues. 
  • Invest in cloud-native data management to gain cloud scale and agility, accelerate time to value, and boost ROI. 

In a Nutshell

As business decisions become increasingly connected, contextual, and continuous, it’s time to reinvent the decision-making process. ‍At Wissen, we believe data holds the power to transform business performance and success. Therefore, we possess the art of understanding data and unlocking real value from it.

Reach out to us today and allow us to streamline data management, so you can make use of the best data and make strategic and tactical business decisions.