Originally published in finmaps.com
The most important factor in managing a big data project is to adopt an adequate strategy forward from day one. Obviously, there is big business potential in big data. As a result, the number of chief data officers (CDOs) currently appointed is accelerating, rising from 400 in 2014 to over 1,000 in 2015, according to experts, adding 90% of all major corporations will have appointed a CDO by the year 2019.
Focusing on analytical information value is the main advantage offered by CDOs. However, all entities, enjoying the benefit of a CDO or not, have to definitely find a method to demonstrate the potential of big data. This is where a chief information officer enters the scene.
Now, the question is, what role does a CIO play in relation to defining a big data strategy? How can a CIO actually assist the entire entity to utilize their available information to the very best?
1. Finding the correct type of talent
Tech workers enjoying the highest demand are data scientists. How the best candidates are found and how they are utilized is a task to be revealed by CIOs. What is obvious is the fact that the demand for information is increasing as we speak. One type of strategy is to absorb and take advantage of external talent.
A CIO will never know all there is about data, and such an expectation would be a mistake. However, surrounding yourself with people who have considerable knowledge about data is a must. Recognizing talent is a duty for CIOs in order to make those skills related to the more general entity, and also provide for an environment where smart people are able to succeed.
The labor market is effected deeply from the analytics focus. Reach the year 2020 the demand for specialists in big data will increase 160%, as reported by SAS and the Tech Partnership. Forecasts also indicate around 56,000 job opportunities will be made available each year starting in the new decade to come.
However, rest assured that finding that superstar analyst will only be step number one. Corporations and companies from all walks of life must guarantee the fact that their created insight is completely untainted. Big data provides the opportunity for big business to evaluate a topic from different angles and perspectives, something impossible in the past.
2. Big data value must be proven
Crucial now is a well-planned strategy. How to integrate data sources and how to make sense of relationships resulting from analysis is a must-do for firms, and very carefully indeed. Leaders of the IT industry will be playing a crucial role in this regard. Furthermore, the experience gained by a CIO in directing intelligence projects of big businesses can showcase the fact this individual is very much considered an expert in technology, and rightly so.
CIOs have a duty to respond to all challenges and work in constant contact with the peers involved in their line of business, all with the objective of providing valuable insights in important areas, including performance and experience. Coming to realize misalignment becomes much easier when you have a combined entity structure active and in place for data and the field of your work. Experts are continuously striving to establish a down-to-earth take on their company’s data, with the main intention to understand in more detail the information held by their business.
3. With data science democratized, take advantage
There are many IT leaders who have actually focused their attention on the true value of big data. Such leaders have pinpointed their attention on assisting their businesses to establish and launch data science teams. They are also seeking different methods to best use such skilled expertise, and of course, the analytical data produced by these individuals to thus support the business function better, more effieciently and more effectively.
Major advances have been made in relation to democratizing the data science industry. In this regard, Apache Spark is a tool considered by experts able to allow machine learning become far more accessible to all. Other products can also be used by firms, such as databricks, to launch and manage an interface similar to a dashboard above its Spark network.
Your systems are now embedded with analytics that one day was considered very difficult. Data science teams are now being comprised by major Spark enthusiasts. Many experts in this field are focusing on obtaining data, inserting it into business data systems and information warehouses. For example, data from millions of CVs and resumes can be used to analyze and make better recruitment recommendations for various candidates and companies. Implementing this data actually enables business peers to become better proactively. For those involved in recruitment, such experts can forecast when new trends are about to emerge, be it at any level of sector and individual organization.
The line of business in this regard varies significantly. The only concept needed is to draw huge amounts of information from numerous system sources. This is not ground breaking work, theoretically, yet the biggest challenge lies in who can make such an approach actually work in operation.