Data Management With An Operational Data Store

Businesses today rely a lot on determining potential business outcomes or “predicting the future,” so to speak. Companies take advantage of all the tools at their disposal to ensure that everything goes according to plan and that initiatives result in the expected business outcomes. Although new technologies have led to innovative tools and methodologies that help businesses achieve set goals, it could be argued that all these rely on a single encompassing concept: data analysis.

The argument for big data is a long-standing one, and it’s valid. For one, data allows businesses to visualize current performance against their metrics; consequently, data also shows how businesses should move forward based on insights gained from historical and current data. In this data-driven age, however, many businesses gather data without really knowing what to do with them, not to mention which data would be useful to the business. Another concern is who in the organization has access to the data; for some companies, employees have uncontrolled access to data, even when they shouldn’t. Simply put, organizations realize the value of data but don’t have the required technology and management systems in place to sort, prepare, and analyze data.

Emerging technologies may provide modern, more capable methods of data management, but having an effective data strategy in place remains vital, especially in this age of data-driven businesses.

Data For All?

An operational data store (ODS) acts as an intermediary to a data warehouse, storing pertinent operational data for quick access. By using an ODS, operations teams can get the data they need to make operational decisions while all other data resides in the data warehouse. This helps limit data access to what’s only necessary instead of giving everyone free rein to access all organizational data. A modern ODS also features data tiering to ensure that the necessary data is in the fastest tier and comes with customizable event triggers that will send alerts in case of a data breach or any changes to stored data. Having an ODS in place will also help a business scale its capabilities for both transactional and analytical workloads, especially if an organization is planning to move to a cloud-based platform or a hybrid system that provides the best of both worlds. Due to its popularity, convenience, and relative affordability, cloud computing has quickly gained traction, generating more than $300 billion in revenue in 2020 alone.

Aside from enterprise-grade data security, a modern ODS leverages the power of AI and in-memory computing to allow for millisecond response time for digital applications and real-time integration with multiple systems of record. Essentially, an ODS is a high-performance data store that uses in-memory processing to provide real-time reports and analytics on operational data. This super-fast processing also allows for dynamic aggregation of data for real-time transformation while providing organizations complete control over their data by setting and defining their business policies. 

Automating Data Analysis

With the help of AI and machine learning, data can be transformed into insights fast, with little effort from the user. Big data can be very challenging to manage, and AI-powered techniques help in discovering what types of data are useful to a business. Compared to standard data analysis tools, automated data analysis can decipher data whether they’re cloud-based or stored onsite and can get significantly better insights quicker. It also allows the merging of data from disparate sources and monitoring changes outside the organization’s dataset, allowing for unified data processing that leads to more predictive insights that help in making sound business decisions. Automating data analysis allows organizations to achieve the following:

●      Set and understand data milestones.
Any data that rates consistently high or low against set metrics can be set as data milestones to help in coming up with future marketing strategies. These milestones provide an overview of business performance while also shedding light on areas for improvement and datasets that have been overlooked or need more attention.

●      Discover the reason behind sudden dips or spikes in metrics.
Customer engagement is an important aspect of any marketing strategy because it’s a gauge of how effective it is. Automated data analysis will help measure performance of campaigns across a variety of platforms and help provide insight as to why some strategies are effective and some aren’t.

●      Gain a deeper understanding of the customer.
Beyond engagement, conversions are, arguably, the measure of a business strategy’s success. Conversion rates may change depending on the time of year, how old the product or service is, and other factors. Automated data analysis helps track these changes so a business will know where in the funnel these changes have taken place and the reason behind them. 

The Right Data For All

Data analysis used to be a specialized area that only dedicated data analysts could analyze and comprehend. Major advancement and innovation in data processing systems, however, have made data the core of every business, providing insight into next steps and opportunities that could help companies get closer to their goals. Moving forward, businesses will need to manage the data they gather so that it becomes useful in making informed decisions. Companies without a data management strategy in place should start creating one or they will realize the dilemma of not having one firsthand.

Author’s Bio

Edward Huskin is a freelance data and analytics consultant. He specializes in finding the best technical solution for companies to manage their data and produce meaningful insights. You can reach him on LinkedIn.