The 5 Lessons Your Competitors Can Teach You About Big Data
Posted by Naresh Agarwal on Oct 27, 2015 11:52:00 AM
There is a phrase in business we all know well: ‘Innovate or Die’. While this is true in many regards, we believe organizations should also embrace another, equally important idea: ‘Learn or Die’.
We don’t dispute the idea that innovation is among the keys to survival and success. Yet innovation as a stand-alone is not enough. This is predominantly accurate as it pertains to big data analytics, where the industry is moving so rapidly and technology is being used in different ways and for a variety of use cases. Without taking time to learn what others are doing in big data analytics and how they are using it to drive innovation, companies are perhaps missing out on some important opportunities.
We believe there are 5 key lessons organizations can learn from others in regards to big data:
- Prioritize Impact. We see that businesses often prioritize the wrong things. For example, they may place emphasis on customer acquisition, when they could see greater results by focusing on reducing customer churn rates. Or they may be eager to deploy the latest technology, but not have a clear use case for their investment. Smart companies that take a step back and align big data activities with corporate goals and priorities are the ones that see the most dramatic impact on their business.
- Smart Spending. We all wish we had larger budgets. For most companies, the reality is that budgets are growing slowly, if at all. This means that more than ever, companies need to spend smarter. In many cases, companies cannot acquire the skill and technology they need to tackle all of their big data priorities. In these instances, it often makes sense to work with a technology partner that can bring the right people and technology to the table. Access to a wider and deeper set of skills and technology know-how, plus a high-touch engagement model that fosters collaboration and sharing of ideas, can help companies more rapidly drive innovation.
- Embrace Experimentation. Some companies have adopted a rapid experimentation philosophy in various aspects of their business, for example in product development. However this concept should also be extended to big data analytics. In this type of environment, teams are able to develop hypothesis, then quickly build, test, deploy, and measure results. Because rapid experimentation can be done quickly and efficiently, companies can afford to work on a number of fronts and in a number of areas. This allows the organization to learn from failure without drastic negative consequences if projects fail, and scale the really meaningful findings. To learn more about how your organization can implement a rapid experimentation model, watch our webinar on this topic.
- Look Outwards. As business leaders, we tend to spend a lot of time looking at what others in our market are doing and monitoring our competitors, but good ideas are everywhere. That’s why it makes sense to look at what companies in other industries are doing with big data analytics. For example, predictive analytics tools used by logistics and transportation companies can be applied to a retailer’s supply chain, and some of prescriptive analytics techniques employed by technology companies to drive customer engagement and conversion can be applied by financial services firms.
- Remove Silos. To accomplish many of the items discussed above, organizations need to break down, if not eliminate, silos in their data and within their organization. We have seen that companies that follow this practice end up with more powerful, high impact findings. There is greater faith in the insights that emerge, because the findings are derived from multiple data sources and include input from multiple stakeholders and LOBs. To learn how more about how you can help drive the creation of an open data organization and use big data as a business accelerator, download our white paper.