Conventional Data is more structured. Relational databases and traditional file systems are the main sources in the realms of conventional data. When it comes to Big Data, it encompasses structured, semi-structured and most importantly un-structured data. It originated with search engine companies, which had the problem of querying very large and distributed aggregations of loosely structured data. These aggregations are often incomplete and inaccessible.
In summary it is characterized by Volume (Capacity and Size) Variety (Variation and Range) Velocity (Speed and Rate) and Value- (Importance and Insights)
In yesteryears, there were only a few sources from which data originated. However, new age technology disruptions have led to a massive explosion in data – with new channels of generating data – mobile devices, meters, Social Media, Web logs to name a few. The rate at which voluminous and different forms of data is being generated is phenomenal.
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Barun Lala, Director, Storage, HP India, says, “With the rise of big data fuelled by digital content, mobile growth and compliance requirements, many organizations are struggling to retain unstructured data while ensuring rapid accessibility over the long term.
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With the huge amount of data at their disposal, companies are realising the wealth of information that they can mine to gain insights into their customers, business processes etc., leading to greater demand for Business Intelligence and analytics services. Organizations will have the ability to not only harness data of all types as part of making proactive business decisions but also obtain more useful information for business insights, improve the fidelity of existing information through the process of validation, and improve time to decision making. “Big Data presents many possibilities for enterprises and today’s CIOs understand this and consider Big Data and Business Analytics a top priority,” says Rajesh Rege, Sr VIce President - Data Centre, Cloud and Virtulaisation, Cisco India and SAARC.
Challenges for SMBs in Leveraging Big Data
Realistically, a Big Data situation occurs largely in larger organizations due to their internal complexity and scale of operations. It may not be a problem for SME. They are more likely to be seeking solutions for issues like setting up IT infrastructure. However, they can make use of Big Data to get the data foundation layer right first as their business grows.
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Jyothi Satyanathan, Director, Mid-market and Inside Sales, IBM India South Asia, says, “Many SMBs have struggled to adopt big data and private cloud solutions due to lack of in-house skills and expertise to design and maintain commodity hardware-based systems.” He says, “IBM is addressing these issues with new Power Systems that don’t require specialized skills and offer the latest advances in virtualization and automation to speed private and hybrid cloud creation. Recently announced, IBM's Power Systems for SMBs come optimized with IBM Big Data analytics leveraging the same technology used in the Watson Jeopardy Challenge – bring that level of advanced technology to SMBs at a price point consumable for them.”
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The approach to address the issues of Big Data for an SMB, will be pretty much the same as for any large organization. Just because the organization is of small scale and size does not necessarily implies that it cannot land into Big Data troubles or they cannot use Big Data for their competitive advantages. “What is important is to create the groundwork for tomorrow because they need the ability and expertise to implement big data strategies if they want to cut costs and improve profitability for their organization. The way data is increasing and open source tools are mushrooming, SMB’s need to think about Big Data as the new essential for innovation and profitable growth,” says Deepinder Dhingra, Head of Products & Strategy, Mu Sigma.
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Abhay Koranne, Associate Vice President - DWBI, Collabera says, “Big data might not be a matter of immediate concern for many SMBs, though there could be a few exceptions. For some, the traditional data store and BI tools would do. However, the power that the new-gen tools bring in from a processing and storage perspective can be harnessed by SMBs to further their business objectives.” An SMB should start with a well thought out Big Data strategy, investing in on-premise storage or BI tools at the beginning and evaluate how things go. They can use cloud based storage and BI services as well. One other aspect that an SMB needs to focus on is to manage the data better.
Open source software (SaaS), cloud based hosting are few catalysts for Big Data adoption among the SMB sector.
Big Data analytics can bring deep insights and new perspectives such as better risk management, improving marketing campaigns effectiveness and making supply chain more robust.
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SMB’s can take significant advantage of Big Data. Big Data actually creates a level playing field for SMBs to compete with large enterprises. Big Data analytics also enables SMBs to improve their overall customer experience by performing “social listening” – or collecting unstructured data insights from social media and pulling out specific comments and conversations that directly affect their line of business. Raj Neravati Chief Operating Officer at Cigniti Technologies, says, “SMBs with a meticulously tested big data application will ensure the investments on the project yield the right set of analytics to make insightful decisions for a successful business. SMBs cannot afford to compromise on the cost of quality in particular
to the data under scan.”
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One myth regarding BIG data is that the technologies available are expensive and they actually don’t give any results. That usually happens as people start looking at working with open source technologies without actually understanding how to use them and then get frustrated. Dinesh Jain, Country Manager, Teradata India Pvt Ltd., says, "What the SMB’s actually need is a platform which they are comfortable working with in terms of technical knowhow and yet gives them the power of analyzing BIG data and generate insights which give them competitive advantages like – fraud management, cost reduction, churn reduction, predictive analytics, etc. SMB’s can also look at financial strategies to reduce capex in their initiatives through leasing or cloud based models. But more important than that is the investment they must now make in developing the right skillsets for handling the analytical and “discovery” based requirements of analyzing BIG data." So, with a proper strategy in place together with a business case and financial model in place along with the right skill sets SMB’s are poised to gain a lot from BIG data analytics.
Big Data and BI strategy
There is a good chance that Big Data for many organizations will end up becoming an IT driven exercise. In order to overcome this challenge, organizations should closely involve their business team to leverage the power of Big data and the benefits it can bring in. The business team should be an integral and important part of the BI strategy.
While drawing up a strategy for BI and Big data, enterprises should keep in mind that managing Big data is as challenging as storing and processing it. From a technology perspective, is it important to keep in mind that traditional BI tools will not be able to address the Big Data challenge. New tools need to be explored. Companies can use cloud based BI for harnessing the power of big Data. In-memory BI tools are a very good option to explore. Also, keeping data in check would help. More often than not, there is data duplication and this automatically ends up increasing the data volume. Data virtualization would help to keep the data volume in control.
Today, enterprises want to be smart. Managing data growth is the number two priority for IT organizations over the next 12-18 months. According to a recent survey by MIT, companies that inject Big Data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers. According to Cigniti, “Rapid adoption of social media, increased usage of analytics (based on trends / patterns), Informed decision making have been the key business drivers for Big Data. Consumers like to have access to real-time information most of the time and smart phone usage has increased the demand and need for this information.”
The approach and the changes that Big Data brings in can be defined by associating with what matters to the line of business you are in.
Conventional analytics have given away to segmented, powerful, targeted, vertical specific tools that you can leverage through Big Data. Analytics can be broadly looked up in 4 categories- Descriptive Analytics, Diagnostic Analytics, Prescriptive Analytics and Predictive Analytics.
From a simple text, numbers driven data analytics to as complex as Spatial Data Management, Machine Learning, Operations Management, Speech Recognition, DNA Analysis, today’s business demands extremely deep analysis.
Data analytics today is a powerful tool that can be exploited by every company provided they have the right approach. The key is to focus on the big decisions for which if you have better data, you have better analytics leading to predictability, optimization, growth & profits.
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CIOs have the responsibility of providing the right tools and infrastructure to enable analytics and business intelligence that will improve business strategy, performance and profitability of the enterprise. Traditionally, the volume of data available to organizations was smaller and more structured. This data was generated from within the enterprise through or financial transactions and was stored within the enterprise’s applications like their CRM or ERP systems. “With the popularity of social media, online banking and shopping and a host of mobile devices sending millions of packets of data every day, the volume and range of data now available to enterprises is immense. Enterprises now have the opportunity to use this data to gain business advantage more than ever before,” says Amit Luthra, National Manager, Storage & Networking Solutions Marketing, Dell India.
Traditional business analytics tools are ineffective in handling Big Data and new approaches that can process unstructured and semi structured data to harness this new opportunity have been developed. They include open source frameworks like Hadoop and NoSQL databases such as Cassandra and Accumulo. Hadoop is able to break up the immense mass of Big Data into smaller data packets and analyze them in parallel. Hadoop is also a cost effective approach to Big Data analytics.
Business Intelligence strategies are no more being driven by conventional data ware housing practices. The new ‘normal’ in BI strategy is the ability to ensure intelligent cataloguing of all the data that is generated form multivarious data sources and drawing real time inferences to accomplish objectives. Companies have realized that Smart Data = Big Data + context + inference + declaratively interactive visualization. Companies are employing Data Scientists (Eg: LinkedIn) to implement big data with the need to model & finally the need to test. Here is where companies like Cigniti help their clients with point solutions on Big Testing.
For example, Airlines are able to optimize the price of each flight for any day of the week through insightful analytics that are generated. Inferences are made in Banks and Financial Institutions to enhance their customer care through a data driven approach of customer feedback across Social and Mobile channels doing away with the front office executive. A retailer is able to optimize the supply chain to hold against for the transportation costs. Similarly Healthcare, Governments, Telecom and other verticals are will depend on data driven analytical decision making.
Best Practices for Absorbing Big Data in BI Strategy
>>>> One should implement Big Data techniques in a ‘lab-like’ environment. This gives the freedom and agility to try new things with it. One should have the right skillset and mind-set to approach Big Data and use it in their business strategy.
>>>> Any company who decides to take Big Data should explore both the commercial and open-source routes of the softwares and frameworks available. There are many vendors that develop and distribu
te open source solutions for Big Data like Cloudera and HortonWorks.
>>>> One can look at using the commodity hardware to save cost.
>>>> One of the most important should be to not work in silos; one should not make Big Data as an IT only project and business should have an equal engagement with IT to develop relevant proof of concepts.
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