In the ultra-competitive markets of the 21st Century, business leaders are always in search of methods with which to gain an advantage over their rivals. There are multiple ways they can go about trying to accomplish this feat. Developing innovative offerings to attract new customers is one strategy that can result in differentiating one organization from others in its field. Refining the current product line to address the changing tastes of the public is another strategy that can be very rewarding. It might be that tightening up and streamlining production facilities and procedures enables a business to offer dramatically lower prices for high-quality goods and corner the market for a particular product.
A common characteristic of these potential strategic decisions is the need for reliable information to guide them. This where the concept of analytics comes into the picture. IT teams have vast amounts of data at their disposal regarding their customers, products, and internal processes. Analytics is a process in which information is examined computationally to uncover useful patterns in the data.
Types of Analytics
There are different types of analytics that look at data with specific objectives in mind.
- Predictive analytics is used to identify trends, correlations, and their causes. It makes use of predictive and statistical modeling to provide its insights.
- Prescriptive analytics is used to predict outcomes and help determine the correct actions to address them. Optimization and random testing are techniques employed in predictive analytics using the power of artificial intelligence (AI) and machine learning (ML).
- Diagnostic analytics uses data to understand why a particular action occurred. It uses the techniques of discovering and alerting about potential issues with querying and drilling down to obtain more detailed information.
- Descriptive analytics provides business intelligence through the use of dashboards and reports. It answers basic questions related to what, where, how many, and when certain activities have occurred.
Based on the kind of decisions that need to be made, one of these categories of analytics can provide those responsible with the information needed to make the right choice and provide a competitive advantage for the organization. Ignoring the potential insights locked in enterprise data resources puts an enterprise at a distinct disadvantage.
Impediments to the Productive Use of Analytics
Despite the benefits that can be gained through the effective use of analytics, not all organizations are making optimal use of these techniques. There are several reasons why the results of data analytics fall short of its promise.
- Gaps in the alignment between the business problem to be solved and the analytical processes that are intended to solve it will result in unclear or erroneous insights. The solution to this issue is to start any analytics initiative with a fully defined business problem based on key performance indicators.
- Connecting the four layers of analytics effectively is crucial for obtaining the maximum benefits from the process. Tight integration is required between problem definition, analytical modeling, the data platform being used, and the chosen technological implementation.
- Expecting too much from initial analytics efforts can cause disappointment and a reluctance to trust the process. Solving problems through analytics requires an iterative approach where improvements are made over time to provide more useful and reliable insights into data assets. The desire to start with an ambitious goal needs to be tempered with the knowledge that it will be necessary to refine the process to attain reliable results.
- The lack of cross-functional collaboration can doom an analytics program to failure. An agile team comprised of data scientists, engineers, and representatives from the business and operations groups are required to iteratively address the underlying problem to find the solution. Leaving out parts of the organization will result in less efficient analytics that leads to compromised decisions.
Addressing these issues when considering how analytics can help a business is a critical step in using it productively. Failure to do so will not produce the level of business intelligence that a more comprehensive approach will deliver.
Performing Multi-Platform Analytics
Database teams are an important part of any analytics initiative. They wield the technological tools that turn raw data assets into the charts, reports, and visualizations that communicate the insights uncovered by analytics. Using the right tools can make a huge difference in the speed and reliability with which the enterprise can perform viable analytics.
There are several reasons why Aqua Data Studio is the right tool for performing data analytics in multi-platform database environments. Its first strong point is the number of database platforms that can be supported by its unified interface. Over 30 different databases ranging from SQL Server, MySQL, and IBM Db2 to Snowflake and Maria DB. A DBA can bounce from system to system without leaving the interface, thereby breeding familiarity and improving productivity.
The most beneficial feature of Aqua Data Studio from the perspective of conducting analytics is the ease with which you can create advanced visualizations that communicate the important points in your data to a varied group of stakeholders. With a good plan regarding how the organization will use analytics and the versatility of Aqua Data Studio, you have the necessary foundation to obtain a competitive advantage for your business.