Data Science: Business Analytics and Big Data

Data Science: Business Analytics and Big Data blog header
Data Science: Business Analytics and Big Data blog header

Data Science: Business Analytics and Big Data

With the advent of Big Data came the design of a discipline created to manage it best: data science. The world of business has long functioned with business analysts to manage their information systems and advise decision-makers. However, where does big data fit into the business realm? And to that end, where is the data scientist in business and business analytics? It is helpful to understand the components – Big Data, Business Analyst, and Data Scientist.

Big Data – What is It?

Naturally, the typical response to the question of what is "Big Data" is "data in great volumes." Per Tech Target, the volume of data is only one defining aspect. Others include:

·       Variety of data types

·       Velocity at which the data are processed

·       Value of the data

·       Veracity of the data

·       Variability of the data

The data come from numerous sources, including transactions systems, social media, electronic medical records, and mobile apps. Some examples are:

·       Unstructured (such as text documents)

·       Semi-structured (what might be found in web server logs)

·       Structured (such as what is found in databases and data warehouses)

The data could also come from multiple sources which aren’t necessarily integrated – giving them another level of complexity.

The Role of Business Analyst describes a business analyst as the individual who bridges the gap between business needs and business IT. They are change agents who guide improvements in service, processes, and products. They work closely with IT to determine what is technologically possible in terms of discerning insights and product development. Business analysts incorporate a broad skill set in their work, including:

·       The capability to define business requirements (necessitating knowledge of regulations and reporting requirements, forecasting, financial analysis, pricing, and budgeting)

·       Analytics and problem-solving

·       Process modelling

·       Effective communication (both written and oral)

Business analysts marry their analytical strengths to their business acumen and savvy. This marriage is to ensure that companies are operating at peak efficiency, have a thorough understanding of their customer base and purchasing habits. This ability also helps to ensure that companies are developing products that provide the best return on investment. Models are used to accomplish these tasks. Such model creation leads us to the last component: data science.

Data Scientists

Data science combines the following as per

·       Data inference (prediction and group differences)

·       Algorithm development

·       Technology

Data scientists work primarily with Big Data to glean such insights (as described above) that business analysts study – customer behaviours in particular. When exploring the data, data scientists make use of inferential techniques such as predictive modelling (e.g. discerning which factors might have the most impact on a consumer's determination to make a purchase) and time-series forecasting. They may also conduct data experiments, typically by way of simulation. Data scientists also manage the functional aspects of technology to create apps such as chatbots (which are rapidly becoming many customers’ first point of contact with an organisation).

The skill sets generally required for a data scientist are as broad as those for a business analyst and include:

·       Mathematical aptitude and understanding

·       Facility with technology and programming (primarily the capability to design innovative technical solutions)

·       Business knowledge

·       Data Visualisation (to accurately represent the findings in a graphical format)

A data scientist must possess the capacity to translate the data into a narrative.  This means the ability to tell a story with data - having the ability to show decision-makers and stakeholders the importance of evidence provided by the data.

How might business analytics work together with data science?

Towards Data Science notes that modern business analysts go beyond reporting results. They also are designers of company dashboards (both static and interactive), mobile analytics, and data governance (how data is stored, managed, and secured). Working within the realm of Big Data and data science in business is a positive disruption – this means taking such traditional concepts such as collaboration and culture, and redefining them. These new definitions are needed for more precise understandings that the data provides. While both data scientists and business analysts share the same bottom line responsibility – informing decision-makers and stakeholders for optimal results – there are key differences in approaches.

Business culture in analytics tends to centre on standard methods of evaluation and analysis, and security. Conventional methods bring an assurance of reliability and validity to processes. Security is critical, considering the sensitive nature of the data many companies collect. Data breaches are not only costly to the company; they are costly to their consumers. Much of a company's integrity is built upon the company's ability to protect its customers from fraud. Business analysts work within these constraints and are often even responsible for their development. However, this is where issues can (and often do) arise between how data scientists and business analysts work. Data scientists, to manipulate the big data, may have to work outside the realm of the company's current business intelligence system. This often means the use of open-source solutions which could create security vulnerabilities. A standard practice in the analysis may not provide the answer to a pressing business question where a more innovative technique will. To fully incorporate business analytics with data science, companies will often find that they must take risks (while still maintaining data integrity and security).

The Value of a Data Science Degree in Business

Business analytics and data science do not necessarily have to remain separate entities; it does mean a willingness on behalf of organisations to be open to new practices (with an eye towards enhanced results). Business Analyst Learnings notes that a business analyst looking to transition to being a data scientist should consider coursework in artificial intelligence (particularly machine learning), coding, and statistics. It is important to note, however, that learning those skills doesn’t necessarily mean a career transition. Aston University lists Data Scientist as a new occupation within business analytics. Considering the fundamental goal – delivering the most innovative, efficient, and optimal solutions to businesses, this incorporation of business analytics and data science is not only compelling but will eventually be required.

Business Analytics and Data Science: Education

Given the advanced skill sets needed for business intelligence and data scientist, Kdnuggets reports that a masters degree is preferred. Any masters programme in business analytics should include the following:

·       Descriptive analytics

·       Predictive techniques

·       Prescriptive techniques

·       Communication and leadership

Aston University offers an entirely online MSc Business Analytics programme that covers the above with real-world scenarios for enhanced analyses and problem-solving. Such a data science degree with the development of business know-how will poise you to become a fundamental asset to your organisation.