The growing volume and complexity of data that companies are collating and analyzing, as well as the empowering of more business end-users to access such insights, have raised the importance of intuitive data visualization interfaces for analytics tools, say industry watchers.
John Brand, vice president and principal analyst at Forrester Research, said there is now more general users without technical know-how handling data analytics than in the past, when such tools were limited to the tech department.
At the same time, the relationship between different data sets has gotten more complex, Brand noted. Traditional data visualization approaches were simplistic in representing the correlation such as through rows and columns on Excel spreadsheets.
Today, though, there is a greater emphasis on integrating data from a wide variety of sources, so new methods of visualizations such as infographics, interactive bubble charts and 3D landscapes are increasingly needed, he pointed out.
As more business end-users get access to analytics tools, the way data is presented will need to enable “cognitive” visualization in order for them to better make sense of the insights.
According to Harvard Business Review,Big Data clearly has the attention of the C-suite and responding executives were very optimistic for the most part. Eighty-five percent expected to gain substantial business and IT benefits from Big Data initiatives. When asked what they thought the major benefits would be, they named improvements in “fact-based decision making” and “customer experience” as #1 and #2. Many of the initiatives they had in mind were still in the early stages, so HBR weren’t hearing about actual business results, for the most part, but rather about plans and expectations:
• 85% of organizations reported that they have Big Data initiatives planned or in progress.
• 70% report that these initiatives are enterprise-driven.
• 85% of the initiatives are sponsored by a C-level executive or the head of a line of business.
• 75% expect an impact across multiple lines of business.
• 80% believe that initiatives will cross multiple lines of business or functions.
Here’s the next big thing in India’s IT space firms that can be global champs in crunching huge volumes of commercially useful data for companies. Think about it: copious amount of data is being generated on social media forums such as Twitter and Facebook.
How can a company get the nugget of gold from this data mine, called Big Data in trade jargon? That’s the job of data analytics firms, where India is expected to play a dominant role.
Today’s marketing departments face many challenges. Organizations are still identifying methods to make their products more customer- and market-driven, while businesses are pressured to drive more qualified leads to their sales teams and to work with product development to ensure they’re delivering the products and services clients are asking for.
Some have identified marketing analytics as a way to resolve these challenges. A recent survey directed by Professor Christine Moorman and Sr. Professor of Business Administration T. Austin Finch with Duke University’s Fuqua School of Business, found that marketing executives in the Fortune 1000 and Forbes 200 plan to increase their spending on marketing analytics in the next three years, some by as much as 60 percent. Many will be starting from scratch, as only 35 percent of respondents currently use marketing analytics.
Marketing analytics used in conjunction with big data will help many organizations properly evaluate their marketing performance, gain insight into their clients’ purchasing habits, market trends and needs and make evidence-based marketing decisions. As one example, look at how politicians are using big data to identify their target audience and reach out to the so-called “silent majority.”
With a tough global economy, and high unemployment rates, employers are literally deluged with stacks and stacks of resumes. That’s where Big Data analytics comes into play. Machines are increasingly reading and scoring applicants for call-backs and interviews. And personality tests are chock full of data, which are then used to predict the suitability of candidates for a specific job based on how they answer a battery of questions.
Seventy-five percent of the 191 top supply chain officers who took part in a June 2012 Aberdeen Group survey said their decision making could be improved with the use of proper analytics, defined as special software tools built to discern patterns or trends in supply chain and logistics operations. Aberdeen Senior Research Analyst Bob Heaney detailed the survey results in a presentation at Dematic’s 27th Annual Material Handling and Logistics Conference in Park City, Utah, where more than 400 people gathered in early September to hear presentations on the latest supply chain and material handling developments and trends.
Respondents to the research firm’s survey said predictive analytical software would help them to achieve cost savings, increase profitability, and differentiate their customer service from that of competitors.
44 percent of the survey respondents are currently using analytics to improve internal processes for forecasting, pricing, and planning promotions as well as for making mid-course corrections. In addition, 37 percent said they are using analytics to optimize inventory based on predictive analytics for customer demand or service levels. Another 35 percent are using analytics to “transform” their supply chains.
A real world example of how leveraging Big Data can solve the complexity around product proliferation by helping companies align product offering and supply chain based on customer-buying patterns.
Big Data: More Than Just A Trend
According to Gartner, unstructured and structured data held by enterprises continues to grow at explosive rates. However, volume and velocity of data – what the business world is beginning to understand as the “Big Data Problem” – are becoming less of an issue than the variety of data. Each silo within the enterprise – operations, supply management, sales, marketing – faces its own data variety challenges, where bits exist in a multitude of formats and types.
Due to the variability of data across silos, systems can’t “speak” to one another, and gaining an accurate, enterprise-wide view of demand and performance seems impossible. In fact, most business and IT managers accept the lack of inter-system collaboration as a given, an inevitable limit that must be worked around.
There is a better way to tackle this challenge in variety and capture the opportunity posed by Big Data.
Patterns And Connections
This Big Data challenge requires solutions that can harness the intelligence from the data and deliver actionable intelligence to the business user. Conventional business intelligence and data warehouse tools aren’t designed to analyze, identify and surface critical data linkages and causality.
Freeing the data to reveal connections and causation through pattern-based analytics solutions will paint a bigger picture – one that can better manage product variants and streamline sales by shedding light on what customers are buying, when, where and how. Currently companies pour their non-standard data into spreadsheets that then require teams of data analysts to interpret and derive meaning from it. This is not scalable and often misses the mark. Big data demands applications that can interpret and deliver immediate actionable intelligence to business users.