The evolution of data platforms is one that has displayed massive growth throughout nearly every industry imaginable. Understanding your data to its fullest extent and being able to extract its value quickly is a key component of what makes it so valuable to organizations. While data management may seem like an ongoing battle, there are steps and philosophies to take to combat some of the challenges. Before diving into some of those strategies, it’s important to discuss potential concerns one may face in handling data management.
Most organizations will run into at least a few obstacles when facing data management initiatives. Here are some of the most common:
- Data Quality
- Unique Organizational Nuances
- Designing Big Data & Analytics Solutions Up-Front
- The Cost and Time-To-Market of Big Data & Analytics
- Privacy & Security
- Technology Barrier of Analytics Tool
- Hidden or Hoarded Data
- Changes
Maintaining usable data which retains security and compliance can be a challenge when some of these issues are occurring. In many cases, they tend to be compounding creating an even larger issue when initiatives begin to focus on getting the most out of the data available. The impact can be costly if data is not maintained properly.
Data platforms have become the backbone of a company’s success. Properly utilizing data can be the difference between succeeding and failing on both a micro and macro scale. While data management might require an initial investment, the amount of time, manpower, and financial savings cannot be understated over the longer term. Taking the data and turning it into wisdom is the next step.
Turning data into wisdom can be broken down into three different chunks: context, meaning, and insight. The context is simply the data provided as a fact. The individual figures provide a starting point to build off as raw material to mold. Data then becomes information by way of organization in a manner that is easily understood and structured. The transition from information to the next step, knowledge, provides meaning. At the knowledge checkpoint, we’re now able to develop an idea of what the original data provided means and can be compared to other data sets. Finally, knowledge can become wisdom through insight. The original data can now be applied and used as an actionable resource to fuel decision making and strategies moving forward.
Another approach that should be utilized is MVP – minimum viable product. This concept, in summary, helps get projects off the ground through iterations built up over time rather than trying to jump straight to the top. By using an iterative process and prioritizing key features and functionalities, this allows an approach to retain agility and adjust to shortcomings as they appear throughout the process to avoid toppling the entire project. What is more, it provides a foundation for data processing, analysis, and visualization while allowing for iterative improvements based on user feedback and evolving requirements, enabling organizations to gather insights, assess user needs, and refine the platform to align with the desired outcomes.
To get the most value out of your data as efficiently as possible, there are several key checkpoints to achieve to have the highest chance of success. Once base level of understanding of what data is available, creating a strategy which can be explained with supporting detail to executives must follow. After this is achieved, value-based execution and review are key to confirm the process is in fact the correct path and functioning as intended. Moving forward, the focus will be to maintain executives’ support and continued iterations to guarantee long-term success. If these steps are followed, value provided by proper data management will prove its worth across the business on both a micro and macro scale.
