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Data Analytics

How is Data Analytics different from Business Intelligence?

The concept of data science has taken the world by storm in the past few years.Whilst data analytics and business intelligence are very closely related terms, there are some key distinctions to be made between these two engaging disciplines.

What is Data Analytics?

Data analytics (DA) refers to the extraction and categorisation of data used to boost productivity and business gains; basically, it is done to identify and analyse behavioural data and patterns. However, the data analysis techniques used tend to differ, depending on the organisational requirements.

What is Business Intelligence?

Business Intelligence (BI) refers to the use of data, post-analysis; it is an umbrella term for a set of processes, architectures, and technologies that transforms analysed data into meaningful information. High-end companies today use BI techniques to make a range of tactical and strategic operational business decisions.

What is the difference between Data Analytics and Business Intelligence?

Though there is an evident overlap between DA and BI, most industry experts understand that, whilst often entwined, each separate term describes a specific function, using different techniques, and serving different purposes.

Firms use DA to draw insights from raw information: in today’s data-heavy market, it is important to break down a mass of data into smaller segments. This helps in understanding the trends or metrics in data – instrumental when optimising processes for a business. DA is the front-runner in determining the efficiency of a business, and is broadly segregated into four types:

  • Descriptive Analytics is used to determine a synopsis of historical data to produce useful information, and to assess future analysis strategies;
  • Diagnostic Analytics is considered an advanced analytics technique. It is used to examine data and find out why something happened. Diagnostic analytics involves data analysis techniques like data mining, drill-down, and data discovery;
  • Predictive Analytics makes use of machine learning and predictive modelling, delving into historical, as well as current data to predict data trends, and plan ahead;
  • Prescriptive Analytics is employed to determine the best course of action or solution for a given situation, and uses diagnostic, as well as predictive analysis techniques.

On the other hand, Business Intelligence comes into the picture at the decision-making phase. Collectively, it provides the historical, current, and predictive analysis of business operations. It encompasses a variety of tools, methodologies, and applications that make it easy for organisations to gather data from both internal and external sources. It is a data-driven analytical tool, and is in high demand today. Enterprises use BI as a part of their basic functioning, for multiple purposes, such as: 

  • Measuring performance and progress towards business goals; 
  • Analysing quantitative components with the help of predictive analysis, business process modelling, and statistical analysis; 
  • Presenting actionable information to help drive business;
  • Supporting better business decision-making.

The scope of Data Analytics

The global shift towards utilising DA, from traditional methods, has had a profound impact on business dynamics. Every day, companies generate a large volume of data, making it almost impossible for those traditional methods to keep up to speed. Therefore, given the volume of data to be analysed, DA is the most efficient way forward. As a crucial component of data science, it has the following benefits:

  • Data cleansing identifies and corrects the errors in data sets collected, and is an important aspect of data analysis. This is beneficial for both business growth and customers, aiding the qualitative analysis of data; 
  • Duplicate data poses a major flaw, so DA works to remove redundant duplicates, making data more precise, saving space, and decreasing company costs; 
  • Advertising becomes more effective by making good use of historic data and online behaviour; machine learning algorithms display relevant, customer-specific advertisements; 
  • Banking risks are reduced by using historic data to help decide loan or credit card sanctions, and identify fraudulent bank customers; also, 
  • Security agencies use DA for surveillance and monitoring, collecting data in order to prevent crimes.

The scope of Business Intelligence

Introduced in 1989, BI was once considered a complex form of data science, though technological advancements have made BI far easier to implement. Data is one of the most valuable resources in modern business, and must be used wisely; through BI, modern businesses are using their data its full benefit.

  • Accuracy in predictions is crucial for business, and BI eliminates guesswork, providing reliable data and the ability to forecast a range of ‘what-if’ scenarios;
  • Speed is always an advantage in business, and due to the prevalence of accurate information in BI, quicker decisions can be made without losing valuable time;
  • Insights into customer behaviour enable those businesses with huge customer bases to better serve their customers, and push up growth and profits;
  • Efficiency is enhanced by segmenting data into simpler, more effective parts, accessible from one data source. This reduces the required time investment, and simplifies the process; 
  • Streamline doperations mean important information can be readily pin-pointed, and BI makes it easier to determine data relevant to the situation at hand; and 
  • Trends are clearer, and easier to analyse, meaning the future of a business can make use of their past and present challenges.

How much do Data Analysts earn?

Data Analysis positions can vary, however a Business Analyst can expect earnings around £75,000. Meanwhile, a CRM Business Data Analyst can earn £85,000 whilst Data Architects can command salaries in the region of £100,000.

How to become a Data Analyst

With data analysis and business analysis positions in such great demand due to today’s data-reliant business landscape, there are far more opportunities to enter into this rewarding field:

  • Degrees in Statistics or Information Technology are usually a minimum requirement to enter Data Analyst work.
  • Entry-level experience(as a statistical assistant or actuarial technician) is essential to foster skills, and help you become comfortable with analytical software.
  • Master’s degrees will give you an edge over your contemporaries, and help you rise through the ranks.

With data analysis and business analysis positions in such great demand due to today’s data-reliant business landscape, candidates can expect to command enticing salaries in these rewarding career paths. If you’re keen to join their ranks, why not consider MSc Data Analytics and Finance, and MSc Data Analytics and Enterprise Architecture available through Edology.

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