From Brexit teething problems to the push for hybrid working and growing calls for action on the environment, management teams across every sector are facing a slew of unexpected and unprecedented challenges right now. However, in many cases, they already have the solution to their problem, yet they cannot access it.
The answer to many critical strategic issues post-Covid lies in the vast oceans of data companies accumulate during their day-to-day operations. Whether it’s consumer purchasing patterns, the ebb and flow of supplier demand or assessing different regional and channel responses to a new global marketing campaign in real-time, clues to the most competitive course of action typically lie within reach. But like a miner who doesn’t have the right equipment to extract valuable commodities from rocky ground, data’s ultimate prize remains out of reach for many companies because they struggle to transform raw information into practical insights.
Often it’s not through lack of effort. All companies see the benefit of being data-led, and many have invested significant resources in data analytics to generate business intelligence. For some, such as banking group RBS, this effort has paid off dramatically: RBS’s adoption of data-driven decision-making contributed to the bank reducing the time to market from months to two weeks. For others, it has led to frustration, disenchantment and pushback. In our experience, problems with data analytics are generally the result of the same core issues. Here are five top tips for fixing those basic problems, helping data analytics deliver at all levels of business management.
1) Be realistic about current data capabilities
Some companies claim to be leaders in the data race – but are barely out of the starting blocks. Both internally and externally, they create a dynamic narrative about data analytics for fear of being compared unfavorably with companies that have succeeded in making the cultural switch to data-powered decision-making. But to avoid wasted investment and wrong turns, they need to be upfront about their organization’s data capabilities. Companies that haven’t got to grips with data are far from alone – a recent Forrester report revealed that 41% of businesses struggle to transform data into decisions. A good place to start is a thorough audit of existing data capabilities benchmarked against best practices. The picture that emerges will help provide a realistic starting point for the journey to become a more data-led organization.
2) Set achievable, rapidly-attainable goals
Companies often try to address data analytics challenges by throwing large sums of cash at big, ambitious data transformation projects. It’s far more effective to start with small, easy-to-control projects and bring them to market fast. This represents a shift away from centralized, hierarchical planning toward a more open culture of iteration and experimentation for many businesses. By starting small and scaling breakthroughs when they occur, companies can keep tight control of the purse strings and provide persuasive evidence of data’s effectiveness.
3) Deliver insights where they are needed
Among companies that have developed data capabilities, there’s still often a tendency to keep it locked up in the data science division. However, data-driven intelligence is most effective when viewed as a team sport – involving everyone from the chief executive to junior staff. A siloed approach to data means that business intelligence isn’t getting where it needs to be. Breaking down these barriers isn’t just about emailing a report to the board. It’s about infusing data into the DNA of the entire organization – so that anyone who needs instant access to insights can easily get them. A key emerging capability that digital-first companies are embracing is embedded analytics, which allow users to access data analysis within their existing workflows without having to switch to a different application.
This ‘democratization’ of data can significantly improve an organization’s chances of achieving specific goals, such as greater levels of new customer acquisitions and even enhanced profitability.
Banking group RBS has seen the benefits of this level of data democratization. RBS marketers can now, without IT intervention, make website updates 42% faster than previously. RBS has, in effect, eliminated data silos and fostered a culture of data-driven decision-making.
4) Prioritize human-led design and data visualization
Data analytics is a powerful way to derive business intelligence, but it’s important to remember that end users are usually time-pressured, multi-tasking people who need to access trends quickly. That’s why it’s crucial to fuse the power of embedded analytics with human-centered design.
A key challenge facing data teams is taking insights and building a best-in-class user experience that enables end users to navigate the data quickly and easily. Within this mix, data visualization is a valuable tool that can be deployed across sectors. This approach has proved effective for logistics companies such as DHL, which uses data analytics across its supply chains to increase efficiency, identify risks, improve customer experience and build new business models. Data visualization such as live dashboards and graph analytics is helping DHL and the logistics sector as a whole combat issues such as long lead times and supply chain weaknesses.
5) Nurture a data-powered culture
Data analytics can transform a company’s performance, but sometimes it forces C-suite executives to confront tough and unpopular decisions. For organizations in their entirety to embrace data-based decision-making, C-suite executives need to foster a culture that trusts the ability of data to deliver.
One way to address this is for the C-suite to consider using people analytics to enhance employee wellbeing. By developing a picture of employee engagement and satisfaction levels, then categorizing those findings according to location or department, managers can quickly identify trends and respond to any emerging issues in real-time.
Deloitte, for example, has noted how effective data analysis can improve employee wellness, energy and performance, while also identifying the causes of employee attrition. Managed sensitively, employees can be helped to understand the power of data at both a personal and organizational level.
One more tip...
A robust, accessible and well-designed data analytics platform can generate insights across every aspect of a business. But the benefits don’t stop there. Embracing the power of business intelligence can help companies decide the precise moment they need to pivot in order to survive.
Netflix is an example of a company that trusted the data when it turned its back on mail-order DVD delivery and embraced streaming. Likewise, a data-powered organizational culture can open up powerful new revenue streams. By ‘productizing’ data, companies can create actionable intelligence that customers will pay for, paving the way to new and unexpected avenues of business growth.