Data Science Consulting: From Big Data to Business Insights

A famous quote states - “If you torture the data long enough, it will confess to anything.” Data is a double-edged sword and hence, needs to be handled by the experts. In late 19th century (and even today too), statistics was the branch of science primarily used to make inferences from available data. The statisticians had the expertise and rigor to conduct the analysis and draw conclusions.

Today “data science” is in vogue. But how is data science different from statistics? Data science is an interdisciplinary approach to analyze complex large datasets. It is a method that blends statistics, computer algorithms, and business acumen to gain insights from a large heap of data. Although we refer to it as a science, data science is actually an art. It is a technique that develops a creative solution for a business from datasets available.

How is data science different from statistics?

Employing data science for business growth is becoming increasingly common. In today’s world, large datasets are easily available. Every human activity has undergone ‘datafication’ i.e., being converted into data. For instance, wearable devices track sleep, exercise, walking; credit card purchases. On production lines, most steps have undergone ‘datafication’ too. For example, in manufacturing sector, parameters such as temperature, pressure, and properties of the raw materials are constantly recorded. These large datasets by themselves are meaningless. But a good data scientist can transform these data into crucial business insights which can then lead to meaningful changes.

Data scientist often use machine learning (ML) algorithms to classify the data, recognize patterns in the data and make predictions based on the data. ML is a branch of artificial intelligence that enables machines to learn from their environment. In other words, a machine improvises its output without any input from a programmer. ML approach is useful for all kinds of data, including the data are generated at a high speed and in large volume. For example, in the manufacturing industry, data points related to the parameters such as temperature, properties of materials are constantly being generated. With the use of ML, floor managers are able to modify the manufacturing process in time and have optimum production.

Undoubtedly, to be a good data scientist, one needs to have expertise in algorithm development. But more importantly, it also takes personality traits such as problem solver, curious, and sharp business acumen to be a good data scientist. A good data scientist has advanced training (but not necessarily advanced degrees) and experience in solving business problems. Data scientist tailor their approach based on the problem at hand, the goals of the department, and available time.

Hiring and retaining a data scientist is a challenge in the current times. On an average, data science jobs take about 45 days to be filled due to the shortage of qualified candidates. The skill sets of data scientists are in great demand and often data scientists are looking for new intellectual challenges. So retaining talent pool of data scientist is difficult. A collaboration with a data science consulting company offers a solution to this problem. Businesses that have their own data science departments also tend to collaborate in order to expedite projects.

Data science consulting companies are problem solvers

A great data science consulting company works with their client closely. The consulting company helps the client to identify their problems and define their business goals. It offers solutions tailored to the client needs and within constraints such as budget, time etc. After data acquisition is done, the data scientist prepare data for an analysis, dealing with missing values or constants, revealing or generating required features. Then the ML algorithms are developed to conduct the analysis and they are validated on real datasets. This process is often iterative as algorithms need to be flawless. Finally, the algorithms are deployed at the client site. The consulting firm teams up with the client analysts to walk every step of the way. It continues to provide services, if needed, even after the machine learning or data science solutions are deployed.

From retail to manufacturing, banking and retail, almost all industries are generating big data related to IIoT or customers and their behaviors. Data science is not hype but an amazing tool that provides businesses information to streamline their business processes. Businesses need to embrace data science in order to create new opportunities for themselves and keep up with the global trend.