Data analytics has been a matter of great complexity. While the sources of data are expanding, the situation only gets even more intricate. Although there have been advancements and innovations in technology to accommodate a wide gamut of databases that are available, there is still a dearth of tools that can effectively handle the data and visualize them.
One of the largest stakeholders in the analytics arena is the technology itself. Most companies may opt for a certain set of tools to mark the beginning of their analytics quest. While this may be true to an extent, tools do not form the most important part of the solution. The pivotal aspect of successful analytics is the quality of data. The quality and structure of data, in parallel with the availability of data, affects the outcomes of reporting.
It should be noted that while reporting is a vital part of analytics, it requires a wide reserve of data. In order to make this possible, several tables comprising complicated logic need to be brought together, making it a laborious task to write them and maintain them efficiently to tackle threats. A data solution that is structured and designed well can handle such a workload, lifting the burden off the data consumers.
Besides, the data may tend to have several downsides that can and need to be addressed. When there is a scenario of missing data, through the right expertise, the data can be retrieved, positively influencing the quality of reporting. In addition, even if the data source holds invalid data that needs to be processed to bring meaningful reports, using appropriate tools, skills, and understanding can make it possible. In this process, the tool can be automated to manage the process of transforming, correcting and filtering the data, making it possible for the users to access consistent data.
While there are several data solutions in the market, it is imperative to understand that no two data solutions are the same. While on one end there may be merely a reserve of raw data, the other end may hold data that is ready to be used through any reporting tool and is intuitive. On a wider spectrum, the data solution chosen plays a vital role in defining the solution architecture, in parallel with the requirements that need to be fulfilled by the data tools. Irrespective of the solution chosen, it is vital to define the needs and formulate the right foundation to have an effective and seamless data analytics journey.