Big Data as a strategic resource 6 / 13

Analytics and business intelligence

There are a number of terms that are used in business analytics and we will look at some of these below.

  • Web analytics
    analytics tools (like Google Analytics) allow companies to track the traffic on their website - for example: what search phrases people used, how they got to the page, what they were looking at and in which sequence, how long they stayed, whether they were convened into customers.

    Such information is very useful to enable companies to track their website effectiveness and customer engagement. In turn, if companies can improve the effectiveness of their website and their engagement with their customers, this should help them achieve their strategic goals.

  • Customer analytics
    Customer analytics enable companies to understand which customers are their most loyal, their most profitable or their most expensive to keep. 

    Data rich companies such as telecom companies and retailers are now also looking at understanding customer life time value, and even identify trigger points when customers are likely to cancel their contracts or to shop at a rival company.

  • Predictive business analytics
    In the simplest form predictive analytics allow companies to use their data to forecast and predict future liquidity and cash flows as well as revenue and profit predictions. 

    More sophisticated approaches use tile cause and effect logic to understand, for example, the impact of increased customer satisfaction on future loyalty as well as financial performance. In one case, a bank found that even though most of its customers were happy, few actually generated a profit for the bank.

  • HR analytics
    Given that people are important assets for companies, companies need to identify who is performing well, who is performing less well and who needs more support or training.

    HR analytics can also be used to identify the best ways to recruit new talent, to monitor development activities, to identify skills gaps in an organisation, for succession planning, and to identify the drivers of staff satisfaction. 

    Google used analytics to identify ten factors that make a good manager, and they now use this insight when recruiting and training managers.

  • Project analytics
    Companies use project analytics to track whether projects are running on time and on budget, and also whether they are generating the desired outcomes. In many cases, simple project management software applications can generate most of the analytics which are required, although for more sophisticated project-specific analytics software may be required.

  • Process analytics
    By contrast to project analytics that look at 'one-off' projects, process analytics are concerned with the day-to-day operations in a company. Process analytics are used to understand which processes are optimised and to identify processes that can be improved or changed to increase efficiency and effectiveness.

    Traditional process analytics start with process mapping (often end-to-end) and an analysis of the effectiveness and efficiency of each of the process steps. Other approaches are TQM, Lean or Six Sigma, which allow companies to measure, analyse and optimise process performance.

  • Supply chain analytics
    Supply chain analytics enable companies to optimise their supply chains; for example, by finding tile most efficient delivery routes, the best locations for their warehouses or production plants, and the most intelligent storage approaches.

    Supply chain analytics can also be used to analyse data from delivery routes to better understand fuel consumption, and to identify potential risks to the supply chain (for example, from disruption to road or air freight links).

  • Procurement analytics
    The main benefits from procurement analytics will be generating significant cost savings in the purchase of goods and services, and reducing the business risks associated with those purchases. 

    Procurement analytics allow companies to optimise, consolidate or standardise purchasing and contracts, and enable companies to strategically source products and services at tile right time, for the right price, and in the right quantities.

  • Marketing and brand analytics
    Applying analytics to marketing and brand building activities enables companies to understand tile effectiveness of their marketing campaigns.

    Digitalisation and e-commerce means that companies can track how many customers and how much business each marketing campaign has generated. Analytics allow companies to identify the most effective marketing channels and marketing strategies. Companies can also use analytics to track brand awareness and brand engagement.

  • Big Data analytics
    Big Data analytics refer to tile ability to analyse larger quantities or more unstructured data (ie data not in a database) such as keywords from conversations people have on Facebook or Twitter, and the content they share through media files (photographs and videos). 

    The aim of 'Big Data analytics' is to extract insights from unstructured or large volumes of data. This can come from a wide range of different sources: for example, from RFID tags, tracking devices and traffic flow, from social networks, from internet search indexes (such as Google Trends), or from the timing and location of cash withdrawals from ATM machines.

    Some commentators believe that big data analytics has the potential to transform the way companies make decisions.

Business intelligence

Business intelligence (BI) refers to technologies, applications, and practices for collecting, integrating, analysing, and presenting business information.

Analytics relates to the use of (a) data and evidence, (b) statistical, quantitative and qualitative analysis, (c) explanatory and predictive models, and (d) fact-based management to drive decision making.

Together, they include approaches for gathering, storing, analysing and providing access to data that helps users to gain insights and make better fact-based business decisions, to improve performance, to help cut costs or to help identify new business opportunities.

Examples of business intelligence and analytics applications include:

  • Measuring, tracking and predicting sales and financial performance

  • Budgeting, financial planning and forecasting

  • Analysing customer behaviours, buying patterns and sales trends

  • Tracking the performance of marketing campaigns

  • lmproving delivery and supply chain effectiveness

  • Customer relationship management

  • Risk analysis

  • Strategic value driver analysis

Overall, a company also needs intelligence about its business environment to enable it to anticipate change and design appropriate strategies that will create business value for customers and be profitable in new markets and new industries in the future.

Not only does a company have to anticipate the future, but it also needs the capability to react to that future successfully.

Evolution of data analysis

In December 2013, the Harvard Business Review published an article by Thomas Davenport called Analytics 3.0 which provides a useful summary of the history and evolution of data analysis, and some ideas about future trends.

1.0 - Business Intelligence

From the middle of the 20th century to the beginning of the 21st century new computing technology allowed large scale information systems to be built. 

The data warehouse captured information and business intelligence software extracted reports. Getting the data into the warehouse in the right format was time consuming, as was tile analysis. 

The information generated was generally past information to improve operational efficiency.

2.0 - Big Data

From around 2000, analysis became more predictive and explanatory. The input data became much broader, encompassing not just the organisation's internally generated data but also external data from sources such as the internet and public data initiatives.

This 'Big Data' was too large for a single server and relatively unstructured, so new software frameworks and class of databases were required. 

The data was analysed by data scientists with computational and analytical skills. Data scientists were able to work on new product offerings and help shape the strategy of the organisation.

3.0

The boundaries of the switch to the future of Big Data already lie in the past, when big high tech corporations such as Amazon used analytics to drive their strategy. 

Amazon's product recommendations based on ones own or other customers purchasing patterns, customer feedback, ability to suggest products to friends, are all features of the Amazon business model that depend of the analysis of masses of data. 

This is a trend that other companies are now following. A large number of the normal activities of companies, groups and individuals leave data trails that can be analysed and used in making business decisions at any level of the business from strategic to operational.

The delivery company UPS has been using analytics in its operations since the 1980s when it tracked package movements and transactions. UPS currently uses sensors in its delivery trucks to record data such as speed and direction. 

This data is used in combination with online maps to redesign delivery routes. In 2011 this reduced the distance travelled by UPS drivers by 85million miles, which was a saving of 8.4 million gallons of fuel.

There are plans to use extend the initiative to update a driver's pickup and delivery schedule in real time.

A key theme in this new use of analytics is that it enables companies to compete not just by improving internal decision making, but also by creating more valuable products and services.