The Executive Guide to analytics trends
In today’s complex business environment, the field of data analytics is growing in acceptance and importance. It is playing a critical role as a decision-making resource for executives, as a way of improving the planning process and as a basis for automating and optimising processes.
Organisations have shifted from simple statistical analysis to more advanced analytics, which allows them to predict future events and outcomes, based on historical data. For example, how many customers will churn in the next 3 months? A key advantage of some of these tools is that they will automatically identify which factors are the most likely to have contributed to the predicted result. The next stage is "Prescriptive Analytics" which calculates the optimal action for each predicted outcome. More on this from our blog
Self-Service Tools for the Business User
The ongoing gap between needing business information and unlocking it from the analysts and data scientists. More tools and features that expose information directly to the people who use it, without the need of IT will emerge during 2016.
Internet of Things (IoT)
The IoT is rapidly evolving from interesting gadgets to real-world applications. Take Health Insurance - some insurers are giving discounts to customers who have wearable tracking devices. GPS and other sensors enable analytics to optimise routes, analyse driving and recommend where to refuel in shipping, road haulage, and vehicles.
And for the first time new network connections are being added faster for IoT than for phones (counting all U.S. carriers, about 1.4 million cars got connected to cellular networks in second quarter of 2016, compared with 1.2 million phones and less than 900,000 tablets, according to Chetan Sharma Consulting). More on this from our blog
Blockchain is emerging as a way to enable businesses to make and verify transactions on a network instantaneously without a central authority. A blockchain is a data structure that makes it possible to create a distributed digital ledger of transactions that cannot be amended, residing on a network of computers. It uses cryptography to allow each participant on the network to add (but not amend) transactions to the ledger in a secure way without the need for a central authority.
Some companies today are experimenting with distributed ledger technology as a secure and transparent way to digitally track the ownership of assets, including digital currencies as Bitcoin. Some companies also see an opportunity to use blockchain to track the movement of assets throughout their supply chains or electronically initiate and enforce contracts. However, it remains in the experimental phase inside many large firms and there are few tested use cases today. more on this from our blog
Embedding The Intelligence
"No-coding-required" apps are one way for companies to enable business users to get the information they need, and in a shorter time.
Machine learning is about creating algorithms that enable computers to learn from experience, resulting in the automation of jobs that used to require human interventions. Companies will recognize that many algorithms can be acquired rather than developed = the concept of ‘Just Add Data’ ! More on this from our blog
Data-As-A-Service Business Model
Companies will be packaging "Data-Streams-as-a-Service" as a new business model. Companies will look to drive value and revenue from their data sets. Organisations like Thomson Reuters, Experian, Nielsen already do this today.
Streaming of data and analytics enables "on-the-fly" responses within the time to complete a transaction - a must-have for rejecting fraudulent credit card transactions, for example, or for making real-time offers to on-line buyers who are predicted to be "borderline" purchasers.
The use of predictive analytics for monitoring to decrease security threats. It is all about being proactive, stopping the threat before it happens.
Anonymised public data, Open Data can be freely used, modified, and shared by anyone for any purpose. The ability to supplement your own datasets with public data to enrich the data you already have to provide greater insights for your business for competitive advantage.