DATABERG
As it stands, 90% of global data was generated in the last two years. As such, data is transforming the decision-making examples posited by business leaders.
Computing giants IBM estimate that 2.5 quintillion bytes of data are created daily. As a result, business has access to a phenomenal amount of information about their client base. Every touchpoint, from websites to apps, social media to transactions, generates valuable data that companies can leverage to enhance operations. Through thorough, structured analysis programs, companies can implement organizational transformation to increase profit. In this article, we examine three decision-making examples powered by big data.
1. Improve operational efficiency
Big data is playing an increasingly important role in operational efficiency. Businesses are leveraging data to model sales strategy, automate production, and streamline operational processes. For instance, Tesla builds chips into their vehicles that capture performance data, which is sent to engineers for analysis. This information enables Tesla to troubleshoot in real time, improving the performance and handling of their cars. Furthermore, this intelligence furnishes the vehicle owner with important service and repair notifications.
2. Deploy real-time data to drive customer engagement and retention
In today’s technology-driven markets, price is no longer the crucial product differentiator – it is customer service. In response, companies have been using real-time data tools to facilitate personalized services. For example, American grocery chain Kroger use big data to offer a customizable loyalty program. By analyzing intelligence collected from 770 million customers, Kroger can generate insights that deploy personalized features to enhance customer retention. Since implementing the scheme, Kroger claim they process 95% of sales through their loyalty program. Overall, this represents an incredible $12 billion in incremental yield.
3. Enhance capacity without additional capital investment
This final case study is perhaps the most exciting. Until now, many C-levels would assume that it was impossible to grow a client base without capital investment. However, big data is transforming how companies allocate resources. For example, telecommunications company Sprint is using big data to identify network errors, optimize productivity and manage resources. This has enabled Sprint to increase their delivery rate by 90% – which represents a significant improvement in customer service, and thus, client acquisition.
How to apply data to generate new decision-making examples
The first step to developing a big data strategy is to identify goals. For example, many teams begin with an “IWIK” discussion. This exercise stems from a simple concept; each team member asks a question beginning with “I wish I knew…”. Often, this technique uncovers key strategic questions that big data can answer, enabling organizations to move towards developing their own data-driven decision-making examples.
Furthermore, targets identified in an IWIK exercise will keep big data projects goal-oriented and focused. When initiating a big data project, there is potential to get lost in data, which can cause diversions. Subsequently, team members can lose sight of the objective. In a new business landscape dictated by analytics, competitive businesses are modeling perception data, transactional information and layers of customer behavior insights before deploying IWIK exercises to define the parameters of their investigation. As such, business should focus less on ‘big’ data and more on ‘valuable’ data.
Lastly, as more businesses move towards data-driven strategy, it is important companies to invest in data literacy. Through training staff, investing in the right analytic tools, and encouraging a data-driven business culture, companies can enable their staff to develop a robust, evidence-based business strategy.
