2016 Predictions For Data & Analytics In Relation To The Skills Gap
In the coming year Big Data technology will continue to be the big thing for business. Experts estimate the amount of data some companies hold could be worth $8 trillion or more. The access to such a massive amount of data can be both a curse and a blessing for companies, as this would help reduce the time take in decision making on one hand and on the other hand increase the need to hire the right kind of resources to draw meaningful insights from this data.
To leverage big data, companies will have to overcome an enormous skills gap in the talent market. In fact, one of the LinkedIn reports predicted 'Data Scientist' to be the job of the year for 2015. While, we at Absolutdata feel 'Data Scientist' to be the job of the century.
In order to gear up for 2016 companies have already started hunting data scientists, and recruiters are scanning the market to fill the gap between demand and supply at a faster pace.
In order to do so, they have to be cautious about the following:
1. Finding the right resources with right skill set
A good Big Data scientist should possess strong mathematical / analytical skills with equal proficiency in handling massive data sets. One must be able to crunch numbers, understand the math behind building the models and should be able to find insights. Sound knowledge of the businesses domain, understanding of customers and processes can help communicate the data finding effectively. It is not just the data scientists who need to rise to meet the challenges of Big Data but managers at all levels also need to have knowledge, skills, and experience to effectively start a carrier in the industry.
2. Training the current team with the right skill set
Often companies choose to use a combination of hiring, retraining and outsourcing to fill the demand supply gap. But getting new salaries approved, working with recruiters, and interviewing and on boarding candidates is a time-consuming process. Companies are training employees with big data platforms so that they are able to analyze large, messy, unstructured data quickly and help make better decisions faster. To ensure that big data creates big value calls for reskilling and retraining employees on different big data platforms so that they can make data-driven decisions. Companies leading the revolution already have an experiment-focused, numerate, data-literate workforce.
3. Outsourcing Big Data needs
A gigantic amount of valuable information can be generated from big data, but accessing this could be challenging and it typically lies beyond the scope of routine business intelligence. Many companies today are partnering with third parties to create and execute big data analytics strategies. Integrating external experts into the big data team may be the best way for companies to stay ahead in this quickly evolving space.
4. Unloading the banal work to the machines / automation
To save the work of manually reading and coding every piece of text which require a good amount of man power companies should automate most of the processes; Algorithms - like robots in manufacturing - are doing the mindless, repetitive tasks of discerning subject matter, keywords and sentiment. Predictive coding, as it's called, frees employees to focus more on case strategy than on the tedium of analyzing every single PDF and email message to figure out if it's relevant to a case. Due to automation people are only integral to the first and final steps - selecting the metric with which they're concerned and then interpreting the statistical correlations. The entire process makes business people more efficient.
Analyzing large data sets-so called big data-will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus for 2016 as long as the right policies and enablers are in place
To leverage big data, companies will have to overcome an enormous skills gap in the talent market. In fact, one of the LinkedIn reports predicted 'Data Scientist' to be the job of the year for 2015. While, we at Absolutdata feel 'Data Scientist' to be the job of the century.
In order to gear up for 2016 companies have already started hunting data scientists, and recruiters are scanning the market to fill the gap between demand and supply at a faster pace.
In order to do so, they have to be cautious about the following:
1. Finding the right resources with right skill set
A good Big Data scientist should possess strong mathematical / analytical skills with equal proficiency in handling massive data sets. One must be able to crunch numbers, understand the math behind building the models and should be able to find insights. Sound knowledge of the businesses domain, understanding of customers and processes can help communicate the data finding effectively. It is not just the data scientists who need to rise to meet the challenges of Big Data but managers at all levels also need to have knowledge, skills, and experience to effectively start a carrier in the industry.
2. Training the current team with the right skill set
Often companies choose to use a combination of hiring, retraining and outsourcing to fill the demand supply gap. But getting new salaries approved, working with recruiters, and interviewing and on boarding candidates is a time-consuming process. Companies are training employees with big data platforms so that they are able to analyze large, messy, unstructured data quickly and help make better decisions faster. To ensure that big data creates big value calls for reskilling and retraining employees on different big data platforms so that they can make data-driven decisions. Companies leading the revolution already have an experiment-focused, numerate, data-literate workforce.
3. Outsourcing Big Data needs
A gigantic amount of valuable information can be generated from big data, but accessing this could be challenging and it typically lies beyond the scope of routine business intelligence. Many companies today are partnering with third parties to create and execute big data analytics strategies. Integrating external experts into the big data team may be the best way for companies to stay ahead in this quickly evolving space.
4. Unloading the banal work to the machines / automation
To save the work of manually reading and coding every piece of text which require a good amount of man power companies should automate most of the processes; Algorithms - like robots in manufacturing - are doing the mindless, repetitive tasks of discerning subject matter, keywords and sentiment. Predictive coding, as it's called, frees employees to focus more on case strategy than on the tedium of analyzing every single PDF and email message to figure out if it's relevant to a case. Due to automation people are only integral to the first and final steps - selecting the metric with which they're concerned and then interpreting the statistical correlations. The entire process makes business people more efficient.
Analyzing large data sets-so called big data-will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus for 2016 as long as the right policies and enablers are in place
