Data Setting the Stage for Future Business Processes
Companies are investing in data platforms – 75% of respondents believe that data will grow in importance over the next 12 months. Only 5% see it as less important. Larger organizations have bigger volumes of data to contend with, and feel that the importance of that data will only increase over the coming year. Finance (55%) and retail (58%) see its value more so than other verticals.
Data is no longer static disjointed bits of information. It is becoming part of the decision-making process through transformation into business insight. However, some organizations are more data-driven than others.
The finance and manufacturing verticals are using data to drive strategic decision-making processes more so than other verticals. Data-driven insight helps improve production/manufacturing operations and processes, supply chain optimization, sales and marketing effectiveness, and resource allocation – providing tangible proof points that digital transformation can deliver on its promise of improved business outcomes.
Those that are more pragmatic with regard to technology adoption are by design the 'show me the data first' types; consequently, they are more data-driven than their early adopter counterparts.
There is a great deal of buzz around how to use data to solve a whole slew of business issues – from improved customer engagement to revealing ways to be more competitive in the market. The noise has reached such high levels that the potential to harness data to drive new business processes and improve operational efficiencies resonates not just with upper management, but across organizations at all levels.
Data Deployment Venues
Currently, the majority of data platforms/services are deployed in on-premises environments. For very large organizations with more than 10,000 employees and those with revenue over $1bn that number jumps to 71%.
However, while large portions of data for very large organizations reside on-premises in both traditional and private cloud infrastructure, it is important to note that these organizations are making use of a wide variety of off-premises cloud solutions for their data needs – even more so than other smaller organizations.
The traditional incumbent database platform vendors continue to dominate, making it easier or preferable for organizations to keep data on-premises. However, large organizations with vast amounts of data will most likely seek hybrid solutions to supplement on-premises resources or ease the transition to off-premises solutions.
Pure-play data analytics solutions are giving way to cloud-based options. Over the next two years, respondents plan to transition their data platforms off-premises and into the cloud.
The success and maturation of cloud technologies are driving the trend to shift data platforms from traditional on-premises software-based solutions to the cloud. All the hyperscaler cloud vendors have data analytics solutions in their product portfolios, and they continue to add new features and functionality to support big-data initiatives.
Relationship operation database vendors have expanded their portfolios to include cloud-base solutions, as well.
Drivers and Inhibitors
The financial sector has the strongest interest in the analytics component of big data. The growing number of digital touchpoints in this sector has created a wealth of data that is being used to predict anything from customer behavior to market trends to fraud detection.
Verticals such as government/education, manufacturing and retail are making use of the data for core back-end business functions. For respondents who work in the telecommunications sector, using the data to improve the customer experience is very important.
The top use case for the government/education sector is actually not analytics, but to optimize IT infrastructure.
There are large volumes of data that reside in static flat files like spreadsheets. That data is not accessible. Additionally, before any analysis can happen, the long and arduous task of data cleaning and preparation must occur.
Data has to be checked for errors. Empty data fields must be corrected. Duplicate data needs to be identified and removed. Variables must be formatted properly. The list goes on. This task could take weeks or even months.
The top inhibitor to using data platforms – accessing/preparing data – is a problem for companies of all sizes. Where the data is stored and how it is stored can create barriers and delays in deployments.