Improving Data Analytics Skills - a High Priority for Businesses

By CIOReview | Thursday, August 25, 2016
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The complexity and ubiquity of data is escalating every day, and simultaneously a dearth of skilled data professionals is being noticed. Enterprises are struggling to find the talent required for executing these data-intensive roles. Data analysis techniques play a crucial role in every technological domain stretching across a large number of jobs. Data is dynamic and in constant motion that makes it often overwhelming for businesses to analyse the copious amount of data. On a positive note, this surging wave of metrics, analytics, and statistics has showed a way for businesses to excel, and the need for improving data analysis skills.

Individuals gain the foresight to implement the appropriate data to the problems or tasks under consideration leveraging good data analysis skills. The process essentially involves converting the information acquired into recommendations, strategies, and productive know-how. The focus on data analytics has increased significantly over the last decade, and businesses have been striving to successfully leverage the scope of data analytics to realize enterprise objectives. Simply gathering data, without knowing the skills to interpret the data will only render the approach useless. Moreover, data analytics assists enterprises to save time and reduce obstacles down the road. Let us discuss about ways data analysis skills can be refurbished to aid enterprises.

Estimation of Appropriate Data is Essential

Companies usually collect the data and quantify it in the form of graphs and charts. When these digits are represented as such, enterprises extract very limited information in the process. This drawback is usually noticed when there is a failure to comprehend the other sets of data. While identifying and analysing the crucial data, other sets of data which could generate useful insight are often ignored. It is essential that the data be disintegrated into smaller components to enable enterprises find the relevance of each sets of data. By spotting all the significant data strains, analysis becomes easier and produces more useful information.

Improve through Learning to Apply

Knowing about the appropriate data is never enough, as only applying it can teach someone to up the data analytics game. The approach should be at applying data analytics to different and specific cases so as to gain more insight every time. Enterprises should encourage their personnel to recognize what is applicable, and what’s not so as to enhance the data analysis skills of each individual. Identifying the applicable data helps companies to bring back value from these techniques and sets them in the right direction.

Data needs to be Structured and Properly Presented

Reading and analysis of data relies on how the data is streamlined and presented. Enterprises often get confused and overwhelmed when dealing with such enormous amount of data. This crisis in comprehending the data can be addressed by organizing the data into relevant sets. Companies have to focus on pinpointing patterns between data so as to make it more insightful. Different teams must share the results so as to enable collaboration on the data under consideration. It helps to segregate the minor elements which are not visible otherwise. Leveraging this information, companies can save them from incurring unnecessary expenses and bring more consistency in the data analysis approach.

Identifying the need for Additional Data

The amount of data in hand can be at times less than adequate. When the data under study is insufficient, companies miss important information which could add value and steer the business ahead. It is essential to incorporate this skill of identifying more data so as to make the data analytics technique more effective. Additional and unnoticed aspects about an instance can be more easily pointed out by analyzing more sets of relevant data. Incorporating this skill can be very resourceful for a data analyst. Moreover, a data analyst should be patient and wisely look at every element concerning the case, which is only possible if more data is analysed.

Data analytics is not a new space anymore, but still, there’s a lot of potential and lot to be learned in the field to make true use of the technology. Data analysts have to adapt to the growing changes in the field to stay ahead of the game. Many courses today offer exclusive know-how on data analytics and the skills required to sharpen the competence for data scientists. This can only be possible if individuals are motivated to adopt the changing trends, besides having deeper comprehension about their professional goals. It only makes sense in such an extensively data-driven world to get acquainted with the data analysis skills as fast as possible.