Big Data and AnalyticsData is the new corporate asset which provides a wealth of information to the companies. Analytics capabilities today are rapidly reshaping industry dynamic and business model. Digitizing customer interactions provides information for marketing, sales and product development while digitizing internal processes can be useful to improve and optimizing operations and production. But adapting to an era of data-driven decision making is not a very simple proposition for organizations. Even companies which have invested heavily in analytics are struggling with deriving effective value with the data they have.Effective use of big data and analytics has the potential to deliver new wave of productivity growth and consumer surplus. Organizations and service providers need to recognize the potential opportunity as well as the strategic threats that analytics represents and should access and close gaps between current infrastructure capabilities and data strategy. They also need to determine which data they would need to create value and address analytical talent and skill gaps in the organization. Big Data Analytics solution provider and organizations need to work closely in order bridge gaps within organizations current and potential capabilities and implement a sustainable and scalable big data analytics model. The MarketAccording to Dresner Advisory Services’ market research (as reported by Forbes), 53% of companies worldwide are currently using big data and analytics with Telecom and Financial Services industry being the fastest adopters. Asia-Pacific has a 44% adoption of big data and analytics. The report also states that data warehouse optimization is the most important use case as of 2017 followed by customer/social analysis and predictive maintenance.India’s Analytics Market Size by Industry Organization Barriers to Adoption of Big Data Analytics1. Lack of proper vision and strategy for data and analyticsSenior management’s involvement plays a critical role in defining the company’s analytical efforts. Moving to a model of data-driven decision making culture requires leadership to bring in lasting organizational change as well as defining proper use cases across its value chain. Senior management also struggles with developing a long-term strategy around big data and analytics resulting in disorganized adoption of analytics in their business.2. Right organizational structure to support analytics The other big barrier companies face is designing the right organizational structure to support data and analytics in their organization. Many companies struggle to incorporate data-driven insights into their day-to-day business processes. These include tracking the business impact and making existing processes flexible enough to respond to new data-driven insights and upskilling employees to engage in data analytical processes. Even though some organization may be comfortable with using analytics in certain business areas, it is yet to filter through the entire organization.3. IT InfrastructureA company’s IT infrastructure is the backbone through which it accesses and processes data to develop relevant insights. Many organizations are concerned about their ability to choose the most effective systems for their analytics requirement. Businesses need to carefully plan and design the changes in IT architecture, making sure their systems are able to handle the all data sets relevant to their business. Future Outlook of Revenue and SpendsAccording to a research published by IDC, the worldwide revenue from big data and analytics will become $203 billion by 2020. Banking sector will see the fastest growth in data and analytics solutions. The same study also mentions that by 2025, we will produce 180 zettabytes (180 trillion gigabytes) of data of which large companies (having employee strength of above 1000) will generate 154 billion in revenue. Software investment will grow to more than $60 billion in 2020 and hardware investment will reach $29.9 billion. US will be the biggest market for Big Data and Analytics at $ 95 Billion followed by Western Europe and Asia Pacific. IDC Worldwide – 2020 Big Data and Analytics Spendings New criteria companies are looking for while selecting a Big Data Analytics vendor1. Cloud Based Big Data Analytics ServiceCloud computing models can potentially accelerate the scope and scalability of big data solutions. Cloud offers flexibility to access data, deliver insights and drive value. Though cloud based big data solutions have been in the market for the past three years, it is now that the companies see its true value and consider it as an integral asset in their analytics journey.2. Need for interoperabilityMost organization still support heterogeneous IT infrastructure and may include MPP databases as well as traditional data sources. Big Data and Analytics application must be compatible with the existing company IT infrastructure and data environment. Companies are looking for vendors who will be able to work with their existing IT ecosystem without forcing major changes. As IOT progresses, there will also be an increased amount of unstructured machine data. The big data solution should also be able to transform these data for analysis. 3. AI in AnalyticsIncreasingly companies are looking for AI driven big data solutions as companies move from descriptive and predictive to prescriptive analytics. AI especially data science and machine learning changes the way data is acquired, managed and analyzed. The use of natural language processing also simplifies data delivery and visualization process.4. Cyber Security in AnalyticsGiven the rise in cyber-threat to company’s critical data, organizations are getting more vigilant towards acquiring solutions which provide effective security measures in them. Though most big data solutions do support cyber security, it is very rare that a single solution covers end to end security. Big Data vendors should gear up towards making their security strong by understanding the treat and compliance requirement for each industry. ConclusionThe challenges towards enterprise wide adoption of Big Data and Analytics is more business driven than technology driven, the major barrier being lack of strategy and data governance. To tackle these business challenges, it is imperative that organizations look towards building big data and analytics Centers of Excellence (CoEs) within their organization. A centralized big data CoE is the bedrock for establishing a data-driven company that treats data as a strategic asset. An Analytics CoE is essential to accelerate big data adoption across the business value chain. It helps in standardizing analytical methodology, tools, policies/protocols, and processes across the organization as well as help in establishing critical success factors and ensures data quality and data security.