What is Database Analytics?
Companies have access to lots of data; but unless they can analyze it to create insights, it's just unconnected pieces of information. Before databases, digital information was stored in files, making it impossible to find relationships among the data points. After databases became a standard vehicle for information storage, data points could be connected to produce business insights.
As the volume of data grew and the number of databases multiplied, IT faced a different version of the same problem. How could the data be merged so it could be analyzed for business intelligence? After all, everything was in a database. But that was the problem. Getting results could take hours as the system chugged through hundreds or thousands of records. Even optimized databases couldn't process the volume of data fast enough.
That's where in-database analytics can help. It provides an alternative to traditional database processing, which uses significant processing overhead.
What is In-Database Analytics?
Analyzing data doesn't require large databases. Individuals can analyze their financial health using a spreadsheet, but the more data there is to process, the more computing power is needed. In-database analytics processes data within a database. Large datasets do not have to be moved into applications for analysis. Instead, the analytical logic is part of the database. Changing the analysis model facilitates data retrieval and processing.
What are Analytical Databases?
Databases may be transactional or analytical. Analytical databases are read-only, meaning data can only be extracted. Unlike analytical databases, transactional (OTL) database handle operational applications which require read and write capabilities in real-time.
Pertinent data is copied from transactional to analytical databases, making it possible for multiple users to perform queries or create reports without impacting the real-time performance of transactional databases. The data is continually updated, so query results are up-to-date.
Analytical databases are designed for big data analytics and business intelligence. They offer faster response times, more efficient maintenance, and better scalability. The best type of database depends on the intended use.
Why Use Database Analytics?
In 1987, Tom Peters published a book entitled, Thriving on Chaos. Among his insights was the need to base business decisions on data, not anecdotal evidence. But, data must be up-to-date for business owners to be agile enough to survive in the midst of chaos. They can't wait for hours to make decisions. That's why businesses need database analytics.
Increase Data Availability
Data is available across the enterprise. Instead of adding to a centralized queue, analysts can query the databases for information. Marketing can learn about customer demographics to develop more targeted campaigns. IT can process network data to identify performance or security concerns. With an analytical database, consistent data is available to anyone who needs it, enabling organizations to base decisions on a single truth source.
Improve Database Performance
Analytical databases are designed for faster performance. Their structures are optimized to sustain a minimal level of performance regardless of the workload. Well-managed databases are updated frequently to ensure data integrity. No matter where a query originates, the results will be based on up-to-date information that has been adequately vetted.
Enable Better Decision-Making
Companies perform "what if" analyses to assess risks associated with a specific strategy. With analytical databases, the scenarios can be tweaked, and results returned quickly, enabling decisions to be made at a moment's notice. The larger the dataset, the more reliable the results. Decision-makers become more agile in their ability to adjust to changing market conditions.
Choosing an Analytical Database
To realize their benefits, databases must be optimized for analytics. Optimization can be achieved by:
- Data storage methods such as columnar databases
- System memory usage for in-memory processing
- Data warehousing for single platform use
- Architecture designs such as clusters
Each method has different strengths. For example, columnar databases are designed for speed, whereas architectural databases address volumes of data spread over multiple endpoints. Before deciding on an optimization method, consider the following:
Volume of Data
Companies accumulate data from everywhere -- websites, mobile apps, in-store questionnaires, and third-party suppliers. There's so much data it becomes impossible to manage. Organizations need to look at the amount of data they collect and determine what information is required. No one wants to reload data because of a faulty design.
Quality of Data
GIGO is an acronym in data processing circles that means "Garbage in, garbage out." If the quality of data going into the database is poor, the quality of the results will be the same. Inaccurate or unreliable data jeopardizes the viability of the analytical results. Companies need a centralized system that functions as a single source of truth to ensure decisions are based on comprehensive datasets.
Presentation of Data
Decision-makers can get the information they need from reports, but they can see the implications of the data when it is visualized. Organizations need to take the visualization of data into account when deciding on a database management system. For example, what types of graphs or charts will be used? Will the data come from multiple sources?
Sources of Data
Normalizing data across multiple sources can be a significant hurdle for many businesses. Pertinent data may be stored on different systems and in varying formats. Unless the data is converted into standardized forms, it is impossible for analysts to retrieve useful results. Companies should evaluate the location and data formats to ensure the analytical databases contain all the required data.
Accessibility of Data
How will the data be accessed? Will remote employees need to access the information? How will data be secured at all endpoints? Answers to these critical questions ensure compliance with all cybersecurity regulations. Employees need access to information to make data-driven decisions no matter where they are, but organizations need to ensure their sensitive data is secure.
As companies grow, so does the data they store. The complexity of collecting, storing, and retrieving data can become a data management nightmare. Make sure the analytic database design can scale.
Following Best Practices
Choosing the right analytics database is only the beginning. Now, businesses need to establish the best operational practices to ensure that the careful evaluation of database criteria is not wasted. Based on the Harvard Business School research, four challenges, if not overcome, will result in a failed analytics implementation.
- Remove silos. The design and implementation must be integrated into company-wide operations. Sharing data and data needs must start with breaking down roadblocks to collaboration.
- Collaborate. Business and technical personnel come from different cultures. Both sides need to work together to identify business priorities and technical resources. Learning more about each area of expertise makes it easier to work together for a common goal.
- Understand basic data science. A lack of understanding of technology is frustrating for everyone. Technical personnel needs to understand basic business concepts, and business leaders need to grasp the fundamentals of data analytics. All parts must find a common language in which to communicate effectively.
- Accept negative outcomes. Data analytics creates transparency, which can result in unexpected or unwanted results. It can disrupt traditional thinking and highlight misconceptions in business strategy. If leaders ignore or undermine results, they minimize the value of data analysis, making the entire exercise futile.
Creating a well-designed plan for data governance can help address these implementation concerns and minimize disruptions in a live environment.
Following these best practices can enable companies to create business value through data analytics.
Read our comprehensive Guide about Data Governance :
Looking to the Future
Because technology does not stand still, data analytics continues to evolve. The future of data analytics will see expanded use of artificial intelligence and machine learning. More complex operations will be developed as the worlds of data and analytics become more closely tied.
New opportunities surrounding IoT data streams can provide operational insights, but they complicate the process of data collection and validation. Businesses will need ways to process larger datasets faster, so data-driven decisions can happen in real-time. Making better decisions quickly enables companies to become more agile, giving them a competitive advantage.
Decision-makers must have all the necessary information in an easily digestible format to make sound choices. Growth in decision intelligence will augment the decision-making process. With sufficient quality data, applications can identify the most viable solutions given a set of scenarios.
Without data integrity, data analytics will fail to achieve its full potential. Companies will question results, and investments in technologies will be marginalized. Data will continue to become more complex, requiring better methods for processing structured and unstructured data.
Given the future of data analytics, companies need to ensure that their data is well managed and adequately curated before using it to make critical business decisions. Inadequate or inaccurate information leads to faulty decisions that can have long-term effects on a company's viability.