A 2015 PwC survey of 1,300 CEOs in 77 countries, ranked data mining and analytics as the second most strategically important digital technology and organization capability, only behind mobile technologies for customer engagement. CEOs also believe that data and analytics is the most important capability for delivering a better customer experience and business efficiencies.
Data alone does not deliver insights. And analytics that are not shared and easily accessible across the entire organization will not optimize delivery of business outcomes and operational efficiencies.
Today, there is a customer gap. Data without insights has no value. According to IDC, on average, companies are only analyzing less than 1% of data available to them. What's the impact of not analyzing 99% of data available to businesses? Research shows that 77% of customers say they are not engaged with the companies they do business with.
Big data is the oil of the 21st century. But for all of its value, data is inherently dumb. It doesn't actually do anything unless you know how to use it. Oil is useless thick goop until it's refined into fuel. Big data's version of refined fuel - proprietary algorithms that solve specific problems that translate into actions - will be the secret sauce of successful organizations in the future. The next digital gold rush will be focused on how you do something with data, not just what you do with it. This is the promise of the algorithm economy. - Peter Sondergaard, SVP & Head of Research, Gartner
The opportunity for organizations and businesses to deliver value by using algorithms is enormous. But to do so, businesses must develop their analytical muscles. They cannot develop algorithms unless they're able to capture, analyze, and broadly share insights across their entire ecosystem of stakeholders - employees, partners and customers. Gartner forecasts that 7 out of 10 companies will have analytics centers of excellence by 2017.
How are business today using analytics to gain a competitive advantage? To learn about the state of analytics in business, we surveyed more than 2,000 business leaders worldwide to discover:
• The changing role of analytics in business today
• Areas where analytics usage is on the rise
• How high performing organizations approach analytics
In this research, high performance teams are defined as those who rated their business performance as much stronger than the competitors. You can learn more about the research methodology here.
According to the research, here are some of the key findings:
- Analytics jumps to the forefront of business strategy. 90% of high performers say analytics is absolutely critical or very important to driving the company's overall business strategy and improving operational outcomes.
- Analytics use cases expand dramatically. High performers are 3x more likely than underperformers to be heavy analytics users, gleaning value via analytics in 10 or more disciplines. On average, high performers analyze more than 17 different kinds of data -- almost double the number analyzed by underperformers.
- The era of real-time analytics begins. High performers are 5.1x more able than underperformers to gather timely business insights from their current analytics tools.
- High performers embrace a culture of analytics. Adopting analytics for the everyday user, top teams are 2x more likely than underperformers to say half of their employee base uses analytics tools.
I analyzed the research summary and here 17 important takeaways:
1. The number of data sources actively analyzed by high performance businesses will grow by 150% by 2020 (from 20 sources in 2015 to 50+ by 2020).
2. Lack of automation leads analytics pain points. Here are the top 10 analytics pain points:
(1) Getting all the necessary data into one view is manual
(2) Too much data is left unanalyzed
(3) Spend too much time updating spreadsheets
(4) Analysis is performed by business analysts, not end users
(5) Turnaround time to get answers is too long
(6) Data is not customized to the end users
(7) No on-demand/mobile interface access insights
(8) Business users struggle with trusting business outcomes
(9) No self-service interface to easily build reports
(10) Critical business questions go unanswered
Automation of data consolidation and customization, from trusted sources, and real-time reporting on mobile devices is key to cultivating a data-driven culture.
3. Analytics is critical to driving business strategy. High performers are 8.2X more likely to view analytics as critical to driving strategy and improving operational outcomes. 84% of high performing organizations say the importance of analytics will increase substantially by 2017.
4. Increased investments in analytics on the horizon. High performers are 6.4X more likely than underperformers to increase analytics spend by 50% or more by 2017.
5. By 2017, business leaders will invest more resources in data and analytics in these areas:
(1) Tools and technology (51%)
(2) People (35%)
(3) Training (35%)
In my opinion, all line-of-business leaders must invest in hiring data and analytics specialists, including data scientists. High performers will also invests in appointment of chief data officer (CDO) and a centralized analytics hub to streamline adoption of best practices and business function specific analytics training. Investments in CRM and analytics platform will democratize the capture, customization and deliver of insights to citizen analysts, leading to significant improvement in business agility and achievement of desired outcomes.
6. High performers are 4.6X more likely to use data to drive business decisions versus simply keeping score. Underperforming organizations are 5.7X more likely to rely on their gut instincts, instead of data, when making strategic business decisions. In my experience, as a former chief customer officer and CMO, only data-driven organizations are able to achieve sustained relevance in business.
7. Analytics is finding its way to every corner of the business. Here are 10 benefits of using analytics:
(1) Driving operational efficiencies
(2) Facilitating growth
(3) Optimizing operational processes
(4) Improving existing products, services and features
(5) Identifying new revenue streams
(6) Generating new ideas and innovating
(7) Monitoring customer behavior
(8) Predicting customer behavior
(9) Improving employee collaboration
(10) Improving the speed and accuracy of decisions
In my experience, high performing data-driven organizations are able to graduate from descriptive use of analytics (describing the past), to predictive use of analytics (identifying future trends based on regression analysis), to prescriptive use of analytics (changing behavior based on prediction, aimed at increasing the likelihood of achieving desired outcomes). To achieve highest level of analytics maturity, businesses must adopt a team approach towards creating and maintaining a data-driven mindset and company culture. Winning organizations put the customer at the center of all their decisions and use data to ensure transparency, accountability and results-oriented discipline is collectively managed.
8. High performing organizations analyze nearly 2X more data sources as compared to underperformers.
9. Here are the top 10 data sources used by high-performing organizations:
(2) Research data
(3) Transactional data
(4) Commercialized data
(5) Log data
(6) Enterprise system data
(7) Event-driven data
(8) Social media
(9) Partner data
(10) Call Center notes
10. Sales, marketing and services lead the analytics adoption revolution. 74% of sales leaders will be using sales analytics by 2016. High-performing service teams are 19X more likely than underperformers to be outstanding at using analytics. And 54% of marketers believe analytics is absolutely critical to creating a cohesive customer journey.
11. Speed, convenience, and relevancy is true differentiation. High-performers are 5.1X more able to glean timely business insights from analytic tools. In my experience, organizations that are able to use speed as differentiation are most likely to meet or exceed both internal and external customer expectations.
12. High performing organizations are 3.5X more likely to use mobile analytics, including reporting. You must be able to make intelligent decisions regardless of your location.
Mobile does not equal smartphone or tablet. Mobile means the ability to work well in transit. Mobile is the ability to work anywhere, anytime, not tethered to your desk. Mobile analytics must deliver insights to employees in transit.
13. Top teams have complete executive buy-in on analytics. 90% of high-performers have their executive team committed to the success of analytics tools and technologies to help business success. Here is an important question that all executives must ask themselves - how can customers trust us if we are making well-informed decisions? And in today's hyper connected economy, how can anyone make well-informed decisions without data? To earn the right to your customer's future business, companies must use data to develop insights, make rapid decisions and actions, and deliver real and timely value to their customers, partners and employees.
14. Data must be accessible, understood and useful to all employees. High-performing companies are 2X more likely than underperformers to at least half of their employee base uses analytics tools. In my experience, training and empowerment of all employees is key to scale, as long as right tools and business processes are in place to be inclusive of all employees. Often organizations will limit the visibility into analytics and access to tools to only management and business analysts, and by doing so, limit the insight and full potential of the entire organizations. The importance of systems integration, data quality, data consolidation and customization and mobility are key to democratization of insights. Here's why an analytics platform is key to success.
15. High performers are 15.5X more likely than underperformers to always collaborate with other diverse roles in the company around analytics. This is a critical takeaways - the lines between sales, marketing and services is blurring. Customer expectations demand businesses to rethink and redesign their silo line-of-business architecture and instead build a fluid and highly integrated model, whereby contextual intelligence defines the next best action. Insights must be shared broadly in order to maintain high performance - imagine running a relay race, there analytics is the baton. To win the race, the baton must be passed gracefully and on time.
16. The top 5 factors that go into the decision about analytics tools to use:
(1) Speed and ease of deployment
(2) Ease of use for business users
(3) Self-service and data discovery tools
(4) Mobile capabilities to explore and share data
(5) Cloud deployment
17. High-performers are 4.8X more likely to say that mobile capabilities to explore and share data are absolutely critical when selecting an analytics tool.
18. 92% of high-performing organizations strongly agree that harnessing the power of analytics is strategic to future success.
19. High-performers are 5.3X more likely to believe that analyzing unstructured data (example: social media) is key to unlocking deep insights into customer behavior. In my opinion the combined use of structured and unstructured data is the only viable path towards development of predictive and prescriptive uses of analytics. Go where the conversation is, listen and learn and then deliver timely, relevant and specific value to your employees, partners and customers.
20. The path to becoming an analytics high performer:
(1) Drive smarter strategy - use analytics to drive strategy and measured outcomes
(2) Broaden your view of analytics use cases - collaborate across lines-of-business
(3) Create an analytics culture - democratize access and use of analytics tools
(4) Invest in analytics - invest early and often to ensure competitive advantage
(5) Embrace emerging technologies - continue to develop analytics competencies
According to Constellation Research, 90% of the worlds data was created in the last 12 months. Customers, partners and employees are creating more data than ever before. We are at the beginning of the data science revolution and survival and relevance depends on greater and more efficient use of analtyics. Companies can succeed by investing and developing a data-driven culture using analytics tools and technologies.