By Andrea Alfieri, Head of Marketing
Over the past several years, the market for data discovery tools has grown at 2.5 times that of the overall market for Business Intelligence Software and analytics platforms (IDC). Likewise, BI and analytics remain the highest priority for Gartner clients worldwide again this year. So it’s an understatement to say that data discovery and BI are very hot market segments.
Reflecting those trends, Gartner even changed the primary focus for its annual Magic Quadrant for Business Intelligence Software and Analytics platforms to concentrate primarily on self-service data discovery and platforms that could be used by business managers without IT involvement.
What are data discovery tools/what is data mining software?
Used for data mining and knowledge discovery, data discovery tools enable users to explore and examine data to uncover trends and insights which may not be visible on the surface. Often such tools are implemented by individual users or teams within an organization to meet departmental needs.
While there are many clear advantages for line of business managers and data scientists to use their own data discovery tools without IT’s involvement, and no one can dispute their rapid growth over the past several years, it’s important that companies don’t lose sight of the value of full enterprise BI and Analytics platforms, the best of which now include self service data discovery as a key component.
To put the issues into proper perspective, consider the following five key factors that need to be evaluated in the process of choosing the right mix of data discovery, BI reporting and analytics functionality:
1. Who’s got the correct numbers?
As siloes of self-service data discovery crop up around a company, there’s going to be a great deal of confusion and faulty decisions from different results as those various departments apply their own customized models, from their own sources of data that have not been validated and are often inconsistent with other data sources being used elsewhere in the company. What looked like incredibly insightful analytical results back in one department’s conference room will suddenly look inaccurate when compared with another department that applied its own data discovery from questionable data.
That scenario then becomes the source of many a headache at the C-level when executives compare and contrast the two sets of results. They’ll likely recall that there were both compelling reasons and solid logic behind the search for a single source of truth in the early days of BI. But if the underlying data is wrong, then the entire exercise is flawed and the results will be equally as wrong.
2. Data Siloes Cost Money and Lead to Bad Decisions
When there are dozens of data analysts around a company relying on their own siloes of data to make crucial decisions, without the valuable input of other departments’ findings or properly governed central consolidation, costly errors are bound to result. Take for example if sales prioritizes closing a million dollar deal with a customer that has not been paying its bills for the past two quarters, or if marketing spends most of its budget pushing an item that is about to be dropped from the product line.
3. What’s good for one department, probably isn’t for the full enterprise
Analyses that benefit the entire company with real long-term value usually require far more sophisticated and complex modeling than departmental data discovery by casual business users allows. And the reverse is true as well, since a single insight from one department will probably be based on models and data that won’t scale to benefit the entire enterprise.
4. The productivity trap
While on the surface it would appear to be a good idea to empower inexperienced business users with self-service data discovery tools to conduct their own analyses, building morale and allowing the occasional “ah-ha!” moments, monitoring their time spent doing so usually reveals an inordinate number of hours wasted on irrelevant analyses. And that time, multiplied by five or six departments, can spell the difference between black and red ink at end of the quarter.
5. Repeat of the Excel nightmare
With the recent proliferation of departmental data discovery, too many companies are creating chaos from thousands of business users running hundreds of their own analyses. They are essentially repeating the nightmare of Excel spreadsheets everywhere, which modern Business Intelligence Software and Analytics platforms are supposed to replace. Haven’t we seen this movie before? And don’t we remember how badly it ends?
So how should businesses approach data analysis?
What’s become increasingly evident in recent years is that data discovery tools need to be an integral part of an enterprise BI system if they are to truly deliver the flexibility and speed required by users, while also safely and productively delivering holistic views of the business at an enterprise level, full trustworthy data consistency, and the correct level of data governance.
And it doesn’t end there. Tools must go beyond pure Business Intelligence reporting to offer the ability to predict future outcomes, deliver comprehensive planning capabilities and support better, more informed decisions business-wide. Only then can the true potential of data be exploited.
This is the essence of a successful Decision-Making Platform, which combines data discovery, business intelligence, analytics and planning, drawing validated data from a single database.
By Pietro Ferrari, CTO, BOARD International
I recently read an article on the development of modern music that resonated with my thoughts on where the fields of BI and analytics are headed. At the dawn of our digital era more than half a century ago, a man named Robert Moog invented the most commercially viable and popular music synthesizer that forever improved the art of music production.
Rather than replicating versions of individual instruments – strings, brass, percussion – Moog designed a system in 1964 that allowed musicians to recreate the sounds of all of them. And by synthetically replicating every possible musical tone, he broke down the barriers between individual instruments, allowing musicians to mix and modulate multiple sounds to create entirely new ones in revolutionary new compositions.
The benefits of data-driven decision-making
Recent meetings with BOARD enterprise customers around the world have illustrated a similar revolutionary convergence in which they have benefited from what had been individual products (BI reporting, forecasting, planning, and predictions) being combined into a single software platform. They too have broken down the barriers between those individual components, giving them a powerful multiplier effect that is far greater than the sum of individual parts.
By aligning strategy with execution and finance with operations, they are now able to make strategic decisions for the entire enterprise with a degree of reliability and accuracy previously unknown in the BI/Analytics realm.
Industry Analyst Wayne Eckerson of the Eckerson Group has documented similar findings in his new report “Decision Making Platforms – Driving Decisions from Insights.” Eckerson writes that in decision-making software, “the same integrated repository of data used for reporting and analysis tasks can be used by visual discovery tools, data science and planning and budgeting tools. Rather than create duplicate data sets for each functional area, a decision-making platform uses one data set to support all workloads.”
“Similarly, planning tools set targets that can be incorporated into dashboards and scorecards. And planning spares BI tools from having to create a clumsy what-if analysis without detailed models that come from planning and budgeting tools.”
Using decision-making tools in practice
As an example, Eckerson sites executives of a major US university trying to understand the potential impact on infrastructure and resources if it grows its student population by 10%. “Performing this ‘what-if’ analysis is a manual process that requires a team of expert data analysts. They need to spend weeks combining and crunching data from many different sources. The result is a static document with no ability for members of the executive council to adjust the scenario.
“In contrast, with a decision-making platform the executives themselves can create various scenarios using a point-and-click interface. They can adjust variables in the models, click a button, and see the impact on other variables, such as staffing, class size, teacher-student ratio, and costs. The application is ready and available when they want to use it; it doesn’t require a team of data analysts to customize the data set and visualizations.”
What Robert Moog and earlier engineers did for music production half a century ago is now being done for enterprise-class decision making. And, just as music composition was revolutionized then, the productivity and accuracy gains to corporate adopters of decision-making platforms is transforming the BI market, delivering far more than the mere sum of those platform’s parts.
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Digiterra recently assisted one of South Africa’s largest financial service groups with a reporting and dashboarding solution, which enabled them to get operational and strategic insights from their IT Service Management information.
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Business intelligence project for Western Cape tourism
the Business Challenges were:
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