Big Data Analytics by Vertical Market Maturity
Premise
The primary value of the results in this part of the survey is for vendors. Vendors can use this data to prioritize their go-to-market strategies by industry and tailor their solution sets to the critical needs of each industry. Customers can also use the data to evaluate their progress relative to their industry peers.
Maturity by industry is a function of customer and prospect skills, competitive drivers within the industry, the nature of the data in question as well as the complexity of the analytics required, and most importantly the problem that the prospect or customer is trying to solve. Identifying where customers are in their journey and helping them to reach the next level is key to vendor success. Maturity by industry is ranked below:
The Impact of Big Data Analytics on Vertical Industry Business Operations
Big Data Analytics is designed to improve business processes by enhancing the quality of information that drives the business decisions that inform and optimize these processes. Big Data Analytic providers need to tailor their go-to-market to specific industries and solution sets that are critical to that industry and are addressable by their solutions at this time. Consider customer and prospect skills, competitive drivers within the industry, the nature and scale of the data in question as well as the complexity of the analytics required, and most importantly the problem that the prospect or customer is trying to solve. Big Data Analytics deployment is still in its early stages, so meeting customers where they are and helping them to reach the next level in their journey is key to vendor success.
The maturity of deployment varies between industries, as a function of the value (i.e. return) of customer behavior and business operations data, skills and experience with analytics, and general comfort deploying advanced technology. Big Data for customer-related analytics is a common theme among most leading industries. Big Data Analytics is generally more mature in Banking and Finance, Telco and Media, and Manufacturing than other industries.
Banking, Finance, and Insurance
Banking and Finance is the most data-driven industry with complex transactions, as well as significant levels of regulation and compliance requiring risk management testing and analytics. With large numbers of customers with significant assets there are also many cross-selling opportunities.
Telco
Telco is a technology-driven business with complex customer metrics relating directly to profitability – churn, usage rates, personalized services, and pricing. Network complexity and high SLA expectations require telcos to analyze network performance in a more sophisticated manner to ensure service assurance to their customers.
Manufacturing
Manufacturing has a long history of deploying complex analytics on large data sets for R&D and product design with high performance computing tools and systems. Predictive maintenance based on sensor data from equipment phoning home from the field also has implications for future product designs. Supply chain analytics are critically important to a range of manufacturers, including consumer packaged goods companies.
The second tier of mature industry sectors includes Government, Retail/Wholesale, Healthcare, IT Technology, and Business Services.
Federal Government
The Federal Government especially is significantly investing in technology for surveillance, customer service, and in areas relating to fraud detection and criminal activity. Smart Cities, Defense-related analytics, and advanced Basic R&D are also areas where the Government invests in Big Data.
Retail
Retailers are using customer analytics to better understand and act rapidly on buying behavior, sentiment analysis, real-time shopping experiences, mobile applications, etc. that give retailers an edge are important. Big Data Analytics is also used in Supply Chain and merchandising.
Health Care
Health Care (including Pharma and Biotech) is emerging as an important sector for analytics with a concerted focus on patient outcomes and better analytics in support of best practices and remote health care options.
IT Technology Providers
IT Technology providers are very experienced in deploying analytics for engineering and science in R&D and product development.
Business Services
Business Services includes consultancies and other service providers who conduct Big Data Analytics and Research for their customers.
Industry Laggards
Industries that apparently are lagging in Big Data Analytics adoption include Transportation, Travel and Hospitality, and Energy and Utilities. These are capital intensive industries with a history of conservative IT investment, onerous data volumes and type challenges, and tendency to embrace change slowly. However, the Smart Grid and the Digital Oil Field are examples of challenges to the status quo where Big Data Analytics can play a pivotal role in the future. This is fertile territory for the Internet of Things, where sensor-based data will become better integrated into field-based business operations.
Conclusions
Big Data Analytics is designed to improve business processes by enhancing the quality of information that drive the business decisions that inform and optimize these processes. Big Data Analytic providers need to tailor their go-to-market to specific industries and solution sets that are critical to that industry and are addressable by their solutions at this time. Consider customer and prospect skills, competitive drivers within the industry, the nature and scale of the data in question as well as the complexity of the analytics required, and most importantly the value of the solution. Big Data Analytics deployment is still in its early stages, so identifying where customers are in their journey and helping them within that context is key to vendor success.
Methodology
Wikibon recently completed an extensive analysis of the results of a Big Data Analytics survey (n=300 web survey interviews in the US) focusing on current practice and barriers to successful deployments. of Big Data analytics projects. Big Data Analytics projects are those that (1) leverage non-traditional data management tools and technologies such as Hadoop, NoSQL, or MPP analytic databases and/or (2) involve the analysis of multi-structured and/or unstructured data such as clickstream, text, log file, and social media data. An example of such a use case would be the use of Hadoop to store, transform and analyze mobile sensor data. Big Data projects do not include projects involving the use of relational databases to analyze traditional structured data associated (e.g., CRM, ERP, Finance, etc.) Our Big Data clients have received a report of our initial conclusions.
This report digs more deeply into the differences between industries on the maturity (Evaluating, Proof of Concept, and Deployment of at least one Big Data Analytics application) of their Big Data Analytics status. The highest levels of Big Data Analytics Deployment were in Banking and Finance, Telecom, Education, and Manufacturing. Transportation, Travel & Hospitality, and Energy/Utilities ranked the lowest.
Definitions
The maturity of deployment varies between industries, as a function of experience with analytics, need for customer behavior data, and comfort deploying advanced technology in general. For our purposes, we had each Big Data Analytics respondent class themselves on the following scale:
- We are currently evaluating Big Data Analytics use cases and vendors/technology.
- We have at least one Big Data Analytics pilot/proof-of-concept project underway
- We have at least one Big Data Analytics deployment in production supporting mission critical business processes and/or applications.
- We have at least one Big Data Analytics pilot/proof-of-concept project underway and at least one Big Data Analytics deployment in production supporting mission critical business processes and/or applications.
