Business

Why Data Science is important and its value to SMEs?

Why Data Science is important and its value to SMEs?
The role of Data Workers and skills needed to become one

 

DATA SCIENCE & BIG DATA ANALYTICS HELPS SME BUSINESS DEVELOPMENT

We discussed what is Data Science and how does that work in our last blog “What is Data Science”. Now we will look at more about the impact of Data Science in SMEs, and major job roles in this new profession.

It is possible that you are running a business or maybe planning to start one or you are considering Data Science as a career choice or perhaps, you are only curious to find out about all these technological shifts happening in the world’s economic landscape. Guess what? You are in the right place. In this blog we are going to discuss why Data Science has become so crucial in our everyday life, also explore what different Data workers do.

The hottest buzzwords currently in the market are Data Science, Artificial Intelligence, Machine Learning, Deep Learning, Neural Nets, Open AI and so on. Some of them are actually correlated or mean the same thing. Among them, Data Science has been arguably the most talked topic in the business community as well among job seekers.

Data science promises us to transform raw data into new riches. By some estimation around 30 to 35 percent companies are in the process to adopt data science. However, the majority of them are large corporations. And that’s not the greatest news as SMEs are the backbone of any economy and they generate huge amounts of data. So, this is imperative that SMEs adopt the data science. The ones which will adopt, should hugely benefit from efficiency and effectiveness of decision making. It is not significantly different for SMEs to adopt data science within their business compared against large companies. The idea is pretty much the same. They need to identify the business goal first and work top down to implement the data science. One of the examples is that many small businesses even fail to have a Customer relationship management software, they can at the minimum use the platform like Slaesforce.com to pin point the areas where they can improve their customer service experience.

Bernard Marr a prominent Data Consultant wrote in his blog: “In many ways, big data is suited to small business in ways that it never was for big business – even the most potent insights are valueless if your business is not agile enough to act on them in a timely fashion. Small businesses have the advantage of agility, making it perfectly suited to act on data-derived insights with speed and efficiency.”

Data science’s fundamental leap forward is the shift from retrospective business intelligence to forward-facing actions. The growing significance of data science has in turn led to the growth and demand of data scientists. These data science professionals have now become integral part of many business, brands, public agencies and not for profit organisations. There is a shortage of analytical and managerial talent, especially as they are in need to make sense of the large amount of data available in the world, according to a recent study done by McKinsey Global Institute. This has effectively become one of the most pressing challenges in the recent times. Moreover, this report also estimated that by 2018 there will be a need of around 5 million new data analysts. It also estimated a need for close to one million managers and analysts who will be able to help consume the results of big data in a manner, that can help organisations reach their strategic and operational goals.

Data scientists are expected to be results driven and curious with exceptional domain knowledge, and that also needs to be complemented by communicational skills as their findings need to be communicated to their non-technical counterparts in business. They possess a strong quantitative background in Statistics and linear algebra as well as programming language with a focus in data warehousing, mining and modelling to build and organise algorithms. They must also be able to utilise key technical tools and skills including R, Python, Apache Hadoop, No SQL Databases, Cloud computing, Tableau, GitHub.

Since increased amounts of data are becoming more and more accessible, small companies will also need to recruit these data workers while currently majority of them are recruited by large tech companies. There are three major types of data workers currently sought after within industries – Data Scientist, Data Analyst, and Data Engineer. We will briefly look into what makes those data workers.

Data Scientist: Data Scientists are responsible for finding the related data and they examine what questions will need an answering. They usually have an eye for business and understands operations as well as analytical skills to mine, clean, and present data. Data Scientists are usually hired by business to source, manage, and analyse big amounts of unstructured data. Then they synthesize and communicate the results to key stakeholders to drive long term and short term decision making in the organization. Skills needed for a Data scientist are Programming skills (SAS, R, Python), Statistical and Mathematical skills and data visualization, Hadoop, SQL, Machine Learning.

Data Analyst: Generally, Data Analysts act as the bridge between data scientists and business analysts to fulfil the gap. They do organise the data and analyse based on the questions provided to them which is aligned with their high-level business strategy. Then they translate technical analysis to qualitative action items and effectively communicate the key findings to various stakeholders. Skills needed to become a Data Analyst are Programming Skills, Data wrangling, data visualisation etc.

Data Engineer: Data engineer manages exponential amounts of rapidly changing data. They mainly get involved with the development, deployment, management and optimisation of data pipelines and infrastructure to transform data to data scientists for querying. Skills needed to be a Data Engineer are Programming Language (Java, Scala), NoSQL Databases (Mongo DB, Cassandra DB), Frameworks (Apache Hadoop).

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