Dawn of new technologies and methods comprising a big data infrastructure and its applications has strongly influenced todays business enterprises. Every coming day, enthusiastic professionals who bring their creativity, intelligence, tenacity, and knowledge carry out, new projects and initiatives successfully, to the increasingly promising area of data science and its related disciplines.
Enormous success and growing interest in big data and analytics has caused an extreme imbalance between supply and demand for skilled professionals in workplaces. In todays scenario, many organizations are prone to missing opportunities and competitive edge, only because their corporate executives do not hold the necessary vision and skills to understand the power of methods and technologies, such as machine learning algorithms applied to a vast and diverse amount of data that would greatly improve business performance.
Exigency to build a Strong Analytics Corporate Culture
In spite of various recent programs by universities, colleges, and other institutions to provide education on data science from functional and technical perspectives, many organizations are still struggling to hire professionals who meet the essential set of requirements that would lead to a superior and effective performance on data analysis tasks. The lack of professionals who meet, partially or completely, the functional prerequisites adds additional challenges for corporate executives and project managers.
Companies looking for data analysts and data scientists, gets disappointed because of the lack of maturity in the use of big data applications and data science. Oft-times, substandard performance of newly hired professionals is a big trouble for the organizations.
To avoid common mistakes and pitfalls in analytics initiatives, companies, during their early stages of the implementation of enterprise or departmental solutions, should try to get the benefits of consulting, support, and training from experienced analytics business service providers, while building their own expertise and corporate culture in this area.
Highly skilled data science consultants can be profitable in this regard. They will not just help to define the right profile of the data analyst role, but also to recognize people who are already part of the company’s workforce and may move to new positions to better contribute to the deployment of analytical solutions. Additionally, skilled and experienced professionals may also be handy in identifying key areas, where big data applications would have more significance, immediate, and positive impact to the enterprise.
Things go wrong when many companies expect that they can get all knowledge and skills assigned to a data scientist role from one single candidate. But the reality is that, as the demands and the nature of big data applications become highly sundry, the requirements for such high-skilled workers to effectively perform these functions may also vary significantly.
Here is how you can frame the Role of Data Scientist
There are two main characteristics of data scientist, that is to connect two extremes- data sources and business areas. The first is called a data science application spectrum, the most important technical knowledge is the ability to deploy, manage, extract, and integrate information from several heterogeneous data sources in a way that they could be enriched, modeled, queried, and visualized by users seeking answers to relevant questions in their decision making processes.
The second is that, there are professionals who directly support corporate or local business executives to get insights from data sets formerly prepared and readily available to them, usually exploring visualization and simulation tools. Data scientist should have an in-depth knowledge of a specific business domain, the role they play within the organization, and how they can help in delivering good results for the enterprise or one particular group or department.
Furthermore, data scientist is also responsible for defining and customizing mathematical and statistical models designed to solve specific business problems and improve the overall performance of business processes throughout the company. Though it looks difficult, but just because of the new discovery tools and software applications, even business analysts have been able to use sophisticated machine learning algorithms in data mining processes that would not have been possible without extensive computer programming and statistical modeling skills just a few years ago.
Organizations should look after the usage and implementation of analytical solutions. There is a more rational approach, that is be to build a team of analytics professionals based on a strong academic background and solid work experience in all data science aspects, which involve areas such as data management, mining, and visualization, as well as application development and deployment. All technical background should be complemented with the domain knowledge necessary to build models, because this combination would be an important part of the collective skills gathered in the team building process.
Look forward for best organization structure
There is another trail too, for C-level executives in a big data environment. It is a common issue with many organizations to find the best organization structure to deliver the promised results and insights of analytics. The most common issues like, there should be a centralized or decentralized approach for managing and carrying out analytics projects, there should be a single technology platform or solution provider or best-of-breed solution for each type of problem or business area, and the implementation of a business intelligence competency center (BICC) led by a Chief Data Officer (CDO) or having the analytics team reporting directly to the CIO or CFO.
There is only one solution on it, and that is to hire the right talent on board which is essential to carrying out any business initiative, especially those which involve high levels of innovation on both technology and business processes. Hiring and retaining the best people is certainly a crucial step in helping organizations overcome further challenges and get the best out of their investment in analytics.
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