Consider for a moment the foundation of any good relationship… There may be a multitude of interchangeable traits that we all look for, but there is only one pillar that supports every good relationship. Trust.

Now consider your relationship with your data. Is it honest and reliable? Is your data quick to help and able to satisfy your needs? Or is it non-responsive and unreliable? Worse still, is it keeping secrets from you? Do you trust your data?

Lack of trust is one of the main barriers to businesses integrating more data and analytics into their strategic decision-making. Far too many businesses are stuck working with poor data, and then interpreting it badly because of the limitations of traditional analytics tools. They may be captivated by pretty charts and dashboards, but without the ability to reliably analyse and interrogate their data, they’re bound to make bad decisions.

The answer? Analytics that can help accurately determine what your plan is, to discover what previously happened, find out why it happened, and then what will happen next. That’s before deciding what the best course of action will be going forwards – the full Analytics Cycle.

Analytics cycle - IBM - planning, reporting, forecasting, predicting, prescribing

Planning Analytics – What’s our plan? 

We create plans, budgets and forecasts, perform analysis and create scenarios to assess the risks and rewards of alternative strategies and actions. But for many, planning is a major challenge. Too much time is wasted on manual processes, collecting, consolidating, and validating data before analysis can even begin.

Spreadsheets are ubiquitous. They’re appealing because of their familiarity but ill-suited to enterprise-scale planning and notoriously susceptible to error. One wrong keystroke and even the most carefully crafted budget or forecast becomes a booby trap.  Like the spreadsheet error that wiped $100m off the value of a business… Or the misplaced keystroke that disappointed 10,000 Olympics fans.

A spreadsheet error at your business might not hit the headlines. But even small errors can cause huge headaches. These errors are indicative of any process that involves lots of manual cutting, pasting and inputting of data.

IBM Planning Analytics takes the pain out of the planning process. It automates calculations, creates a single source of truth and empowers you to undertake more agile and accurate planning budgeting and forecasting.

Descriptive Analytics – What happened?

The rush for self-service data analysis sparked a proliferation of desktop BI tools in businesses of all sizes and industries, producing beautiful charts and dashboards.

However, this democratisation of data has created a multitude of “blind spots” – attractive answers that reinforce a theory; biases that impact which correlations to credit; or data that has been misrepresented in the external aesthetics.

In order to avoid getting blindsided by the “pretty pictures,” it’s critical to know that you can trust the data you’re seeing and move beyond acting on potentially misleading insights.

Cognos Analytics helps you move beyond misleading insights to cut through the uncertainty of “what happened”, while getting deeper insights into the why. Once you’ve strengthened that foundation, then you can move forward and make advanced analytics attainable for everyday decision-makers.

When correlation implies causation

Spurious Correlations – a light hearted look at cause and correlation in data

Diagnostic Analytics – Why did it happen?

Equally important to the ‘what happened’ is the ‘why it happened’ – the root of the problem or the crux of the opportunity – the events that really influenced the business outcome.

You need to detect patterns and relationships and their true drivers to get to the real story. And that’s where diagnostic analytics and data discovery come in.

To get beyond what isn’t known and avoid zeroing in on the wrong information – missing the woods for the trees – Watson Analytics uses smart data discovery and exploration to drill down to the true business drivers to find unlikely relationships hiding in structured and unstructured data. Guided data discovery makes it possible to drill down into exact causes without having to determine the criteria you’re looking for and without knowing what correlations might already exist.

Predictive Analytics – What happens next?

Predictive analytics is a combination of advanced analytics capabilities that span statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring and machine learning. With these capabilities, organizations discover patterns in data so they can go beyond knowing what has happened to anticipating the future.

Predictive analytics can transform guesswork to predictions with a degree of certainty, showing you where you can go based on analysis of trends, patterns, and relationships from data.

IBM’s SPSS technology has long been a gold standard for data scientists wanting to predict what will happen based on the deep patterns in data that can only be uncovered by machine learning. Now those insights are being put into the hands of everyone, not just the specialists, providing the direction necessary to predict future events and to help you make confident decisions about how to move your business forward.

Prescriptive Analytics – What to do next?

So, you know what happened and why it happened. And you know what is likely to happen next. But, you still have to contend with a dizzying array of options to make the decision that’s best for your organisation. And making decisions is never easy. Prescriptive analytics addresses the uncertainty of making the right decision when there are a number of factors to consider.

Prescriptive analytics technology tells us what we should do by recommending actions based on desired outcomes while taking into account specific scenarios, resources, and knowledge of past and current events. Decision processes are automated to increase the smarts and speed of your responses to today’s challenges. IBM Decision Optimization and the ILOG CPLEX Optimization Studio are powerful solutions that help you nail down your decision making like never before.

The new answers discovered here then feed back into the next phase of planning, so that the entire cycle starts again — getting smarter with every iteration.

Smarter Analytics that draw on cognitive services, machine learning, optimisation and pattern-based planning, help you regain the trust that has been lost in your data. By rooting out the hidden errors and bias in your analytics; discovering valuable data patterns and helping to drive your decisions based on a full picture of your data.

By creating a solid analytics foundation, adopting a solution that has the “smarts” to help you determine what your plan is, what happened, why it happened, what will happen, and what to do next, you can ensure that you see a full and accurate picture of your business  – avoiding incorrect, incomplete or misleading data – to help you make better decisions.

After all, trust isn’t a given, and accurate insights shouldn’t be either.

IBM has released two new publications to help you understand exactly how the lifecycle works. To read them in full you can download the PDF here:

Trust your data to drive better decisions

If you want to find out more about how Smarter Analytics could help your business rekindle its relationship with data and deliver more trusted planning, reporting, forecasting, contact  














Posted in analytics, IBM On April 18, 2018 By