Data quantities and the number of sources it comes from are on an upward trajectory – and we don’t see this course changing. As all businesses are encountering this same data bombardment scenario, they can either join the global hunt to cash in on the digital currency for a competitive advantage or sit back and cross their fingers that they maintain their current market standing.

Businesses looking to truly realize value from data – whether it is to increase revenues, create a more loyal customer base, identify new opportunities, and so on – should bring together their big data and enterprise data streams so it can all be accessed and analyzed for insight. A two-pronged approach should also be pursued to gain the most return from data: seeking insights to meet specific business outcomes while, at the same time, employing an innovation agenda to test and play with data to find patterns that aren’t clearly evident.

To set the ground work for analytics project and innovation agendas, the fundamentals need to be in place. They are as follows:

  • Align the business on the goals to pursue. Business units – marketing, IT, and/or finance for example – should agree on and outline mutually beneficial business objectives and outcomes needed to support continuous improvements and drive the company forward. Once this occurs, the proper data and technologies can be selected that can help the company reach the end goal.
  • Establish a data supply chain to liberate data and accelerate it to insight. A data supply chain built on a hybrid technology environment -- a data service platform combined with emerging big data technologies -- enables businesses to move, manage and mobilize the ever-increasing amount of data across the organization for consumption faster than previously possible. When access and interactivity of data is sped up and users can access the data they need when they need it, data can be transformed into insights and value at a much more accelerated pace.
  • Employ a data discovery platform. A data discovery platform is a key component to an innovation agenda as it utilizes techniques such as machine learning to uncover hidden insights in the data.

Start Small

Accenture’s big data research suggests that firms don’t need to pursue a big data project of huge magnitude to see value, they can pursue a proof of concept or pilot instead and also reach impressive outcomes. Once this initial project’s value is seen,  the benefits can grow organically from there.  As all companies and goals are different, determining the first project to pursue could be a challenge for the business to decide on, but where the needle lands should present a solid opportunity for the customers, stakeholders and/or employees.

As some fodder to help make the needed business-aligned decision, here’s a sampling of organizational results that can be achieved through analytics:

  • Sales and Marketing – Increasing Profit –When data insights on pricing are incorporated into daily business processes, strategic decisions can be made with greater confidence and benefits can be realized. For example, a leading European food retailer used an analytics-based pricing strategy and was able to increase the frequency of price updates in real time and gain a better understanding of the competition. The impact was an increase in sales and profit, and improved price perception among customers.
  • Human Resources – Optimizing Talent – Applying analytics to specific HR areas can yield impressive results, including optimizing performance and even predicting which workforce changes and investments are likely to produce the best outcomes.
  • IT – Improving Operational Efficiency – Data insights can help uncover ways to improve operational efficiencies. For instance, a large national agency at a European government experienced slowdowns in utilization, storage limitations and cancelled queries. Through the use of a big data processing solution, storage requirements decreased by 90 percent, Total Cost of Operations dropped, and previously impossible statistical analysis became routine.
  • Supply Chain – Increasingly Intelligent Networks – Savvy companies turn data derived from networked supply chains into valuable information by using analytics, cognitive equipment and smart apps to support decision making as items move along the supply chain from sourcing to production, shipping, storage, sales and fulfillment.  For instance, Taleris technology leverages predictive analytics to analyze data from sensors installed on aircraft parts, components and systems, and makes predictive recommendations regarding aircraft maintenance and operations. The insights allow their clients to turn unscheduled maintenance into scheduled maintenance, identify disruptions before they occur and recover more quickly from delays.

 

Analytics is the core of digital transformation and to start that journey, companies should seek to find the business in their data. While data is on the rise, so are the number of opportunities it is creating for consumers and enterprises. Once insights are uncovered through project and innovation agendas, businesses are then on task to make data-driven decisions and place action behind those decisions – it is at this point that the value of data can be realized. 

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