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16 November 2018

Data Analytics - practical usage

Data Analytics – the process of analysing data sets – enables organisations to make better-informed decisions. It’s a key focus in many businesses today, and covers a range of techniques and technologies including Business Intelligence (BI), Predictive Analytics, Machine Learning (ML) and Big Data Analytics.

Let’s explore these in more detail, and look at their practical usage in a business environment.

Business Intelligence

BI is the process of analysing and presenting data from multiple sources, to help managers and executives understand what’s happening in their business. A number of BI software tools – including Power BI and Tableau – are available, with typical users being business analysts, financial analysts, data scientists and other staff across the business.

The BI process involves:

  • Accessing data from numerous sources including Excel spreadsheets, Access and SQL databases, data warehouses and other applications across your organisation – both in the cloud and on-premises
  • Merging, cleaning and shaping the data to prepare it for analysis
  • Digging deep into the data and analysing it with your software’s standard or advanced functionality
  • Presenting the data in reports, dashboards and other visualizations

The main point of BI is that the wealth of available data in an organisation is transformed into information and insights that are easy to understand – and can be acted upon. It’s used extensively in a wide range of industries and departments/functions. Example of usage include:

  • Finance – creating CFO dashboards, KPI dashboards, sales reports, revenue and profitability analyses, and other financial/business reports for senior management
  • Human Resources – creating HR dashboards that report on staff numbers, staff turnover, salaries, competitiveness of salaries, staff satisfaction and time to hire metrics
  • Retail – reporting on sales, customer trends, buying behaviours, transactions, pricing, inventory levels, supply chain and other metrics
  • Manufacturing – reporting on machine utilisation, staff utilisation, on-time orders, process efficiencies, production yields and losses, mean time between failure, costs per unit, cycle times and stock levels
  • Insurance – reporting on policy sales, premiums, claims, fraudulent claims, time to process claims, trends and customer service/satisfaction metrics

Any organisation or department that has metrics or KPIs – in any sector – will benefit from applying BI techniques.

Advanced Analytics

While BI tends to look at historical data, advanced analytics focuses on forecasting and predicting future events. It’s a range of techniques that include Predictive Analytics, Machine Learning (ML) and Big Data Analytics.

As with BI, the Predictive Analytics process involves accessing data from multiple sources, cleaning and shaping it, and then analysing it for useful information and insights. Predictive Analytics, though, also involves the use of statistical analysis and automated ML algorithms to create predictive models.

These models give a probability score on the likelihood of a particular event occurring – and as additional data becomes available, the algorithms validate or revise the model. It’s a self-learning process that identifies data patterns and makes predictions, which get more accurate over time. Machine Learning allows advanced analytics to be performed on massive, complex data sets (Big Data Analytics) in real-time.

A wide range of sectors and functions use advanced analytics to examine data, perform ‘what if’ analyses and predict likely scenarios. Examples of usage include:

  • Sales and Marketing – recommending products to customers based on previous purchases, cross-selling and up-selling, forecasting demand and targeting customers for specific marketing campaigns
  • HR and Recruitment – identifying characteristics of high performing staff and using that to recruit top talent, forecasting individual behaviour and the impact of new policies
  • Healthcare – analysing patient data to identify trends and red-flags, categorising patients to optimise care, developing precision medicines and personalised care, optimising fitness and training programmes
  • Banking and Financial Services – spotting fraud in real-time, predicting loan and credit card defaults, screening applicants, cross-selling and identifying potential account closures
  • Predictive Maintenance – preventing utility outages and telecommunication network failures, optimising preventative maintenance of critical equipment and minimising downtime

Here at JBI Training, we provide a range of Data Analytics training courses for data scientist, data analysts, business analysts, financial analysts, other business users and software developers. Courses include:

See our Power BI training course (2 days) where you learn the fundamentals of this popular set of Microsoft Business Intelligence tools –

See our Tableau training course (2 days) where you learn to create reports and dashboards from Excel, SQL server and other databases –

See our Python Machine Learning training course (3 days) where you learn Python Machine Learning skills for Predictive Analytics –

See our full range of Analytics training courses here http://www.jbinternational.co.uk/courses/analytics

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