Data-Analysis
Growth-Strategies
Guest-Post
Five Tips and Tricks to Improving Data Analytics
6:46 AM
Every single day enormous volumes of data are generated from tracked statistics to figures and reports. It is becoming increasingly difficult for companies to adapt analytic strategies that will enable them to properly manage and make sense out of big data. The growing challenge of integrating and making sense of huge volumes of data often force many organizations to invest heavily in big data analytics software. Even if you are first starting out your career in let’s say digital marketing, or running your own enterprise understanding and taking action from data is crucial today.
However, even with the best analytics software, many businesses are unable to deal with all the information sufficiently. In order to succeed in effectively handling data analysis, develop a visible analytical strategy and use highly skilled talent to deliver your analytics objectives. Here are tips and tricks that are useful in improving data analytics.
Select the Best Data Analytics Tools
There are many tools available for ingesting, visualizing, transforming, storing, moving and analyzing data. Therefore, it’s critical to identify a suitable tool to solve a specific problem. Selecting the right data analytics technologies is essential in addressing the growing complexity of big data. Keenly evaluate the effectiveness of a data analytics software in meeting the set objectives. Once appropriate analytic tools and database software are in place, you can now focus on crafting a strategy that will deliver your objectives.
Every Data Is Important
Though it’s extremely difficult to aggregate, normalize and analyze huge amounts of data generated from numerous sources, it’s necessary to consider the whole set of data rather than making a premature determination on important and unimportant datasets. Making early conclusions will only subject you to narrow scope in your analysis. Hence you will miss out on a lot of insights. To fully harness the value of your data, employ a data analytics software that will accommodate all datasets and conclusively identify those relevant to your needs.
Decide the Data to Include and Discard Irrelevant Data
Most of the data analytics project will involve huge data sets; this brings in a lot of confusion on which relevant data sources to be used in the analysis. For instance, identifying and eliminating bad data is key to improving the outcome of your data analytic initiatives. Making a decision on what data is required, for instance in identifying factors that can improve customers service, should be pegged on the goals of the analytics. This betters the chances of involving only strategic data that will achieve the right analytical outcomes. In some instances, it might be necessary to include the whole set of data while in others, only a small portion of big data will be used.
Have the Right Talent
Engage well-trained experts who have deep knowledge on creating analytical models that can run predictive analytics and analytics software for huge data. With the increase in the number of data analytics software, both in-house experts and vendors often use these innovative tools without a proper understanding of how they work and the meaning of parameters used. Therefore, it’s important to ensure that the people you work with clearly understand the analytics software and are able to build viable analytic models.
Embrace Analytic Innovation and Diversity
The innovation of big data analytics software has revolutionized the exploration of huge volume of structured and unstructured data, which has enabled businesses to maximize the value of their customer data. More than ever, these innovations in data processing and analytics permit the real-time analysis of incoming data and production of predictions that can be used to make informed decisions. For analytic teams to deliver excellent results, expose them to different development methods and techniques that are available in today’s analytic market. To achieve this, develop an efficient development and production environment that is flexible enough to accommodate multiple types of analytic models.
About the Author:
Robert Cordray is a former business consultant and entrepreneur with over 20 years of experience and a wide variety of knowledge in multiple areas of the industry. He currently resides in the Southern California area and spends his time helping consumers and business owners alike try to be successful. When he’s not reading or writing, he’s most likely with his beautiful wife and three children.
Robert Cordray
Twitter- @RobertCordray
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