Data science is getting huge, exponentially. The basic idea is that every online activity leaves a digital trace, which can be transformed into smart insights. Data scientists have become so important for businesses that Thomas H. Davenport has called it to be the “Sexiest job of 21st century”.
Various researches have shown that there is an acute shortage of data scientists which will intensify in the future. And combining them with decision scientists, they not only help to produce a working model but also aid businesses to make careful data-driven decisions.
Data Analytics bringing Subconscious to the Tangible World
Most of the data analytics problems start off muddy with just a hunch or feeling. These are questions which do not have a concrete answer until analytics provide heuristics, rules and math based algorithms to create patterns. Then programmers and coders translate them into conclusive machine language to operationalize it into systems.
Data Science and Analytics Driving change in the Business Models
Analytics of Big data is not just a science anymore, it’s about making informed decisions with a backing of concrete user behavior data. Here are the steps to convert insights into actionable data:
- Select a consumer base and ask the relevant question: All businesses need to define their USP and know their market. They need to focus on specific business problems to tackle instead of a wholesome approach.
- Collect appropriate data: Big data is not that expensive and intrusive these days. But often, such data is not representative of the specific business problem you need to work on. Carefully analyze the data before investing time in it.
- Optimize data: Transforming and cleansing data is time consuming but important to generate valuable insights.
- Define response variables: A detailed definition of business needs and goals will assist you in defining factors on the basis of which analytics will be tendered.
- Create models: Use predictive analysis to generate algorithms and models to trim your research.
- Create easily consumable formats: Highlight response variables and features to make reports easily understandable.
- Publish results and define alerts: Share your analysis with relevant departments and program alerts to notify you about business prospects.
- Keep your data updated: Refresh the data and incorporate new information as early as possible. Latest insights keep your analytics updated and relevant.
Increased Collaboration and Enhanced Work Culture Steering Future Innovations
Data analytics is an interdisciplinary and multi step approach. It requires alliance across various departments of a business. Five data scientists pressed on the need of collaboration, in an interview by IBM, as below:
- Projects involving innovations are directly impacted by the quality of teamwork. Businesses need to improve team coordination, communication, support, balance, effort and cohesion for a better Analytics solutions.
- It is important to enhance relationships between departments to build real products faster. Align the goals of team with that with other groups to invest in the right direction to solve business problems efficiently.
- There needs to be closer interaction, both formally and informally. When working in isolation data scientists often result in analytics which are ideal and utopian. In addition to buying big data, collect real time data from seminars and conventions with easy and customized polling to motivate real world participation.
- Bridge the gap between development and deployment. Collaborate data team with developers’ team in the same physical space, using common tools and environment. Instead of limiting it to words and blogs, implement it in reality.
- Refine the skill sets of data scientist to make him fit within a challenging project outside his domain. Successful collaboration helps in enhancing the breadth and depth of data scientists’ skills to better understand the ins and outs of a business. It makes him aware of the current process and aides to fit the data and translate this opportunity into technical specifications for computer engineers and programmers.
Data Science refers to the application of math and tools on data to extract insights. The problem is that this data should be clearly defined, but in the real world there are multiple external factors, which are otherwise assumed as constants. To understand and derive insights one needs to appreciate and think along the business context, which needs interdisciplinary approach consisting of multiple skills.
