In 2018 and beyond, organizations will begin to realize how important real-time analytics is.
Data comes at you in real time, streaming into your organization like a waterfall.
Hidden within that torrent of data there are some valuable insights; some of them could flag that your company is sinking. Some insights can offer you a lifeboat in the guise of new opportunities, products or customers.
Only by utilizing real-time analytics on this data can you be sure your organization is agile enough to react to, or even predict when, threats and opportunities arise.
1. Reaction Time
Organizations will slow down customer “churn” by using real-time analytics.
It’s easier to retain a customer than it is to regain them a week after they left. But most companies are looking at data that is out of date, it is based on last week, or last month. Measuring month-by-month how many customers they lose means it is too late to do anything to retain those customers.
This is why real-time analytics will be deployed to make organizations more nimble; then they can respond to business events the moment they happen, rather than acting on them retrospectively.
An excellent customer experience is the key; give them value or they will leave. Using streaming and predictive analytics, companies can differentiate and catch them before they fall off.
2. Visualize This
Using visual and business analytics, organizations will “see” in real time.
As a part of the path to differentiation, companies will use visual and business analytics to reflect business information in real time, as opposed to merely reporting on what happened a day, week or month ago.
They will be able to everything in one place, to see what is happening now, and to determine what actions have been or are being taken as a result.
3. Predict That
Organizations will begin to use machine learning to predict significant events while they can still influence their outcome.
A key element of watching out for business opportunities and threats will be the ability to use artificial intelligence and machine learning. Manufacturers, for example, can add value for their customers by using predictive analytics to lower downtime. This, in turn, saves customers money by lowering the cost of insurance against downtime losses.
4. Calculate Everything
New data science tools will reduce the workload on data scientists in organizations.
Demand for qualified data scientists continues to grow and will outstrip supply. Putting predictive models into production so they can be run against data being streamed from the IoT, or elsewhere, is hugely time consuming. The process is error prone and can take months.
Much of the time-wasting drudgery involved in testing and putting predictive models into production will become increasingly automated through new third-party tools. This can help eliminate the chance of errors, will make it possible to harness the value of these models more quickly and enable you to experiment with a multitude of models.
5. No NoSQL, no IoT
Demand for dependable NoSQL databases will accelerate thanks to IoT growth.
The growth in NoSQL-like distributed databases will continue as the amount of data being stored increasingly mounts - in no small part due to IoT devices. The requirement to act on this information in real-time is seen as critical and is a core part of real-time analytics.
The hunt for dependable NoSQL databases from organizations that are likely to survive the next five years may grow frantic as the field narrows; many startups will or have gone under.
6. Translytical Future
The next big step in databases is ones that that store, search and analyze real-time data.
The maturity of NoSQL-like databases will evolve to support compute capabilities, enabling transactional and analytical, or translytical, workloads onto a single data management platform.
Today’s NoSQLs are largely limited to storing info and not doing much else. Next generation databases will store and analyze data; and soon computation will be available within these databases which will make them more suitable for hybrid workloads to gain rapid insights into the data stored.
Check out our predictions for other industries in 2018 by clicking below.