wiseminer data intelligence software. addressing the big data challenge

Wiseminer Informática LTDAWhite Paper

WISEMINER DATA INTELLIGENCE SOFTWAREAddressing the Big Data Challenge

2014

www.wiseminer.com+55 21 2711 4359

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Wiseminer Informática LTDA

Addressing the challenge

Before tackling the issue of Big Data, CIOs must (if they don’t already) know that there is no silver bullet. Empirical evidence and academic research show that jumping into a long-term, complex (and hence, expensive) Business Intelligence program will more often than not end in frustration. The difficulty of meeting the real business requirements and the temptation to deliver a solution that is way too complex and not flexible enough for most users will sap the credibility of most IT departments.

The solution is often not that difficult to implement, but it takes clarity of vision to address the four key elements needed for a successful Big Data initiative: using the right methodology, speaking the business language, aligning users around the solution and selecting the best tool for the job.

Landing the right methodology

Finding the right methodology for a Big Data initiative is a key step to mitigate risks. The more integration, code developing and customization needed, the bigger the risk a project will end up behind schedule and over budget.

The best methodologies are also the most straight forward ones and should enable the solution to be centered on the data handling capabilities. Good sense should be applied and the data gathering should be focused on harvesting the biggest amount of value-added data as possible. Software selection should be done in a way to minimize additional custom development and extra interfaces, and data validation should follow the 5V dimensions of Big Data: Volume, Velocity, Variety, Veracity and Value.

Setting the scene

The amount of data inside corporations is exploding. Managers today must take into account ever changing business and customer needs, reflected in billions of new data points with all sorts of information and trends, and make accurate decisions based on a multitude of inputs. For executes that are dismayed with the Big Data challenge, one thing is certain: the data increase won’t stop!

To explain what we understand by Big Data and to put the challenge in numbers, according to a global study by Intel, every minute there are 1,300 new mobile users connecting to the internet, 100 thousand new Tweets and six million Facebook page views. And every minute 20 attempts of identity theft are detected. Forecasts point to the number of networked devices growing to over 50 billion by 2015. That’s over five times as many inhabitants in the world today.

With Big Data, CIOs are struggling to generate business reports that cut across all the available data and provide insights to business users. While executives are looking for sophisticated forecasts, risk analyses, benchmarks and competitive insights, most business are still struggling with basic operational reports. The question is: are CIOs using their time with data analysis that improves competitive advantage or are they spending all their time chasing after data and firefighting reporting needs?

According to McKinsey: “The availability of data is not the issue. It’s how we can extract information from all of that data and make good business decisions from it.”

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Bringing people together

One common reason why Business Intelligence initiatives often fail is that IT and end-users team members drift apart and end up not agreeing on the most basic requirements for the solution. The notion that both sides (IT and business users) need to work together to achieve the most value out of a technical solution is far from new, but still it often doesn’t happen.

A successful Big Data solution will have a key sponsor that fosters a good relationship between IT and end-users and enables a joint effort to agree on data structures and semantics and the solution reporting requirements. Having a modular data architecture enables business to make the most out of different ways to add intelligence to data entities and slice and dice the data and visualize the results.

Finding the best tool

All the effort of using the right methodology, understanding the business requirements and bringing people to work together will be in vain if the wrong software solution is selected.

The best software tools for a Big Data initiative will be ones that enables decision makers to have flexibility and autonomy to extract insights from the existing data, without the need to request IT to prepare lengthy queries and build complex reports beforehand.

A step-wise approach is also advisable, starting with the review of variables and the analytic data model to be used and executing an initial preliminary data link analysis to test data correlation and historical analysis. After tweaking the model to suit the business requirements and to extract the maximum insight from the solution, a final data analysis model can be built, tested and validated with a view to make it adaptable and dynamic to future business needs.

Understanding the business language

Peter Drucker once said that: “knowledge has to be improved, challenged, and increased constantly, or it vanishes”. But to capture knowledge and generate insights, Big Data solutions first must understand and then speak the business language.

Data modelling cannot be done without first asking the right business questions to enable analysts to build the best data structure and select the right data to bring into the model. Then data needs to be presented in a way users can understand and trust. A disconnect between what an IT solution brings and what business users really care about means a waste of money and time for the company.

To build trust, any decision support system needs to start by creating outputs, from simple reports to sophisticated dashboards, that are as similar as possible to what business users already use on their daily jobs. This simple approach to understand the current business language and using the existing processes will enable a faster adoption of a new solution and a higher level of trust in its results.

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Business users should have an easy to use interface that provides them with the power to engage directly with the data and find answers to their own questions. Users should also not have to switch tools or learn new applications every time market or customer behaviour changes. The best software tools should be flexible and adaptable and expand their processing engine as requirements change.

Finally, the best Big Data software tools should be cost effective and easy to deploy by having an integrated architecture that already capture the biggest number of required capabilities, and minimizing the number of required interfaces to collect the data.

In summary, by following the right methodology, speaking the business language, bringing users and IT together to define the solution and, most importantly, by selecting the best software tool, IT leaders will be able to mitigate risks and implement a Big Data solution that creates real competitive advantage for their companies.

Wiseminer Data Intelligence is a revolutionary application that combines the best of Business Intelligence (BI) and Data Mining into a single and cost effective solution. Using its Data Intelligence integrated platform, users can fulfil multiple needs such as: data processing automation, data flows for extraction and transformation (ETL), link analysis, scenario modelling, data monitoring, fraud detection and predictive analytics. All of this from an easy to use interface with customizable dashboards to enable a quick deployment and a higher return on investment from the software.

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Screenshots

Wiseminer Data Intelligence Software

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August, 2014

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wiseminer data intelligence software. addressing the big data challenge

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