The History of Business Intelligence (the Future)
In parts one and two, we explored the past and present of business intelligence. Today in part three, we discuss how business intelligence may evolve.
Cloud Business Intelligence (Cloud BI)
While most BI tools traditionally were hosted on premise, the increased demand for data anytime and anywhere has led to Cloud BI increasing in popularity. Cloud BI is low cost and extremely scalable and is an ideal option for providing mobile access to data. Despite the likelihood of higher user adoption, the industry analyst Howard Dresner and Tech Republic states that opinion is split on whether business leaders are comfortable holding sensitive business data in the cloud. Although there are concerns, Gartner asserts that 80% of organizations are likely to increase their investment in cloud based products in the years to come.
Collaboration through business intelligence tools has evolved greatly over recent years. According to Deloitte Access Economics, "a collaborative organization unlocks the potential, capacity and knowledge of every employee, thereby generating value, innovation and improving productivity in its workplace".
Collaboration means different things to different people. For some, it is about a one-on-one open discussion to solve a problem. For others, it is about adding notes against reports or storyboarding. Collaboration can tie in strongly with business benefits as staff are not just analyzing data, they are using the data to increase business value (through knowledge sharing which can result in increased productivity).
In addition, activity based collaboration is putting CRM functionality into a BI tool. If a Sales Consultant is down on sales to a particular customer, their Sales Manager can assign an activity to their worker and ask them to talk to particular customers and find out why sales have decreased. The BI tool can then be used for ongoing information sharing and to measure whether sales have increased. Phocas Software will be unveiling new possibilities for collaboration in 2016.
Advanced and predictive analytics allow businesses to use past data to predict the likelihood of future actions from consumers. Take the example of a supermarket. If you have ever signed up to a loyalty program, the business is potentially recording a lot of data on you such as the products you purchase, the days/times of your visits and the frequency of your visits. With this data, advanced/predictive analytics can be used by a business to assess:
- When the customer is next expected to visit the business
- The types of offers they can make to incentivise the customer back
- The expected purchase value of a transaction
- The lifetime value of a customer
Advanced and predictive analytics tools can also be used to carry out a shopping basket analysis for retail (to look for associations within your purchase data) or for profit optimization (to sell the right products at the right price to the right people). Advanced and predictive analytics is about combining a massive set of data to predict the behavior of customers and future usage is likely to increase rapidly.
While the term ‘big data’ has come into prominence in recent years, the retention of data has advanced over successive centuries. According to Poage, the first record of computer data storage was in 1725 when French weaver Basile Bouchon invented a punch card system to record staff attendance at work. Fast forward to 1944 and Fremont Rider, a librarian in Connecticut, USA declared that libraries were doubling in size every 16 years and by the year 2040, over 6,000 cataloguing staff would be required for the Yale University library alone.
In 2015, the International Data Corporation asserts that the volume of data being created is doubling in size every two years. It is now easier to capture data than ever before. Every time you visit a website, download a smartphone app, engage with a social media post or call a contact centre, you are leaving a trail behind.
Applying big data techniques has helped many organisations gain a competitive advantage and present more personalised offerings to consumers. For example, online retailer Amazon understood the value of capturing and analysing customer interactions on their website and developed their recommendation engine to make tailored suggestions to consumers. As a result, 35% of Amazon's sales are through product recommendations which are made by analyzing large datasets or big data. As technology evolves, more businesses will be able to apply big data techniques.
Smart Data Discovery
According to Gartner, by 2017, most BI tools will have smart data discovery capabilities which will expand the reach of interactive analysis. Smart data discovery will track, analyze and create links between data and customer profiles, interests and other behaviors. Pattern detection (trends) will be automatically noted by the BI tool and presented to the user for consideration. It will soon be even easier to prepare data, find patterns in data and share/operationalize data.
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