2015 will be the year that many big data companies will take their big data analytics to the next level by turning big data into actionable insights. This will supercharge the marketing tactics of the business and make data precious than ever. Before organizations rely on data driven decision making, it is important for them to have a good processing power like Hadoop in place for data processing. Having done these the capability to manage these systems effectively helps achieve business success. Read on to understand some important tips that organizations follow for turning big data to big success to achieve desired market growth.
Buildings cause energy wastage of $200,000,000,000 and emit 34% of greenhouse gases every year. The General Services Administration has partnered with a Massachusetts based big data analytics company FirstFuel to save $13 million annually in the energy costs across 180 buildings.
Bayer AG a manufacturer of the allergy drug Claritin is using big data to get ahead of the seasonal trends. Bayer uses a third-party analytics algorithm to analyse the global warming data so that it can model the supply of its allergy drugs based on weather trends and allergy sufferings.
BMW is using predictive analytics to make big money by enhancing future customer engagement.
Big data analytics is bringing in never before seen opportunities for various companies like BMW, Bayer and the General Services Administration to drive positive transformation in their businesses.2015 will witness increased number of big data companies approaching with their ground-worked big data strategies for informed and profitable decision making to reduce operational costs, deliver customer satisfaction and increase efficiency. Big data can be a hindrance if it is not implemented and interpreted appropriately by the big data companies.
“In a world where every click can be tracked and recorded, we shouldn’t be managing customers by putting them into groups of similar people. We shouldn’t be guessing. We should be able to read the signals customers are giving us to figure out what they want. Business win online when they use hard-to-copy technology to deliver a superior customer experience through mining larger and larger datasets.”- said Nilan Peiris, CMO at Holiday Extras
Government and organizations across different industries are moving at a rapid pace to convert data and analysis into actionable insights for profitable decision making. Undoubtedly, big data has helped organizations make incremental big decisions from a customer’s point of view. Big data analytics companies are leveraging predictive analytics to make decisions like “Where is the best table available for dinner on Friday night for the customer” or “What a customer is actually searching for in their store” or “Which size of the dress perfect fits the customer”. There are several other bigger big data decisions in the making like how to get people on Mars or where should the cancer research point for personalized medication or how thousands of Filipinos can be evacuated instantly during a natural calamity.
With unstructured amount of data generated growing exponentially on a daily basis, it has become easier for the big data companies to dig deep into the details for big decision making, however the rise of big data has not put an end to the criticality of turning big data to big success. Big data is intellectually challenging and if a company has to make big decisions with big data it has to cross the challenging barriers to find out-“What the data can actually predict and What it cannot”. Big data companies must collect data without any specific purpose and let their data geeks(decision scientist, data analysts, data scientists, data engineers) play with the data to find correlations that can be commercially useful from an organization’s perspective.
It is difficult to make sense out of billions of unstructured data points (in the form of news articles, forum comments, and social media data) without powerful technologies like Hadoop, Spark and NoSQL in place. Big data is unusable without structure and companies might take years to comprehend the data, and yet might not be able to yield useful insights.
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With all these challenges acting as a backlash against big data, we have compiled 5 tips for turning big data to big success that big data analytics companies must employ to maximize the value of big data -
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Most of the big data companies have wealth of big data right at their fingertips but they do not utilize it effectively. Turning big data into big success is not without any challenges and thus organizations must prioritize their needs for gaining actionable insights. In most of the big data companies, it is not that data is not available; it is that data is not complete, organized, stored and blended right in a manner that it can be consumed directly for big data analysis.
Utilizing big data analytics effectively will help organizations confront novel business problems by identifying any pitfalls in their business plan or help them find out any inefficiencies in their operational processes. Thus, to do so organizations must blend big data from various sources to leverage predictive analytics effectivly. Time factor is of great essence when it comes to big data analytics. For business leaders to make informed decisions they need real time information. It is estimated that a data analyst spends close to 80% of the time in cleaning and preparing the big data for analysis whilst only 20% is actually spent on analysis work. Thus, organizations must make use of effective ETL tools to ease the process of data preparation that requires a less complex IT infrastructure.
The data collection and preparation tools must blend the big data right at the right time from various sources, including public data –to ensure that business leaders have the information correlated at one place for efficient decision making.
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Organizations with a dedicated predictive analytics business unit have a success rate 2.5 times better than those with ad-hoc or decentralized teams. Companies can make the most of big data analytics by have a centralized set-up for the analytics team. This will help them bring together business leaders and big data technology to intellectualise novel business use cases and outline best practices that other teams within the organizations can leverage.
Retail Chain Nordstrom has set up a dedicated Nordstrom data lab that is committed to develop novel offerings that are backed by data-driven actionable insights.
A big data survey found that 74% of the organizations do not have a well-defined criteria for selecting, identifying and qualifying big data business use cases. The survey found that 67% of the companies did not have well-defined key performance indicator initiatives to assess the big data initiatives.
Organizations lacking a systematic approach towards building analytics solutions have deleterious effect on their success rates. A dedicated systematic and structured big data analytics implementation will differentiate the winners from the no-hopers.
Leadership is an important factor to nurture a data-driven decision making culture. For organizations to boast of successfully implemented big data initiatives, they must have well defined leadership roles for big data and analytics.
Big data initiatives of an organization must have the necessary stewardship to make big data analytics an integral part of their daily business operations. Leadership driven big data initiatives help organizations turn big data into big success, however, only 34% of the companies have appointed a strong leader (Chief Data Officer or an equivalent role) for successful implementation of big data initiatives.
Bank of America,a pioneer in use of big data in the US banking industry has appointed a Chief Data Officer who is responsible for mastering the data management policies and standards, setting up the bank’s big data platform, and simplifying the IT infrastructure and tools required for implementation.
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A recent CMO survey found that, only 3.4% of marketers believe that they have the right big data talent.
The war for finding the right big data talent is on, most companies feel that they are losing. Businessleaders find it difficult to acquire the right analytical talent.
So how do companies find the right big data talent?
The market for analytical talent is very constricted, it is critical for companies to adopt various strategies to find analytical talent for promoting business growth. Some of the large companies have taken steps to acquire big data startups or are developing research labs with academic institutions to improvise their process of recruiting right big data talent.
For example, Walmart has set up WalmartLabs in Silicon Valley that is helping the giant retail chain enhance its customer experience through various big data innovations.
Procter & gamble (P&G) has partnered with Google to improve their analytical skills. P&G learns through Google’s expertise in big data analytics and Google gains expertise from P&G in advertising.
Regardless of the various strategies the companies adopt, acquiring the right big data talent is an important factor in turning big data into big success.
With all the above strategies in place, big data analytics companies can ease the process of turning big data into big success.