It’s the mantra of the digital age that big data can address most challenges that impact 21st Century businesses. At every stage of the value chain, from manufacturing to marketing, there is supposedly a data-powered solution that can anticipate outages, drive efficiencies, unlock markets and even increase employee happiness.
In reality, raw data is about as much value as a tree or a rock if you don’t know how to extract value from it. The reason so many companies have struggled with the digital economy is not that they lack data but because they struggle to derive intelligence and actionable insight from it. They are like Texan cattle ranchers sitting on top of massive oil reserves, without having the drills to reach it, the pump to draw it, or the refinery to process it.
In the digital era, the real revolution is not data capture or storage – but embedded analytics and AI/machine learning. It’s the ability to organise, interpret and embed data into existing workflows that has enabled some companies to achieve massive competitive advantages over rivals or pivot their way out of declining industries into fertile new territory.
That’s fine as far as it goes – but what does it take to embrace the benefits of analytics? How do C-suite leaders transform an analogue infrastructure and culture into a dynamic, data-led organisation? Based on our experience of helping clients generate actionable insights from their oceans of raw data, these five questions should set forward-thinking businesses on the right track:
1. Where are you on the 0-10 scale of data maturity?
A robust data strategy starts with an assessment of your company’s current data capabilities. Carrying out an audit of existing data streams and potential data sources will provide the foundation for robust insight-based decision-making. One of the key challenges for companies trying to retro-fit a data-led culture is that they exaggerate their level of data maturity and therefore fail to obtain the business intelligence outcomes they expected (which usually translates into missed ROI targets).
2. Are your ambitions realistic and easy to implement?
Once the C-suite truly gets the benefits of data, there’s a temptation to go all-in – investing in massive data transformation projects that promise to revolutionise companies overnight. This kind of data evangelism is risky because it unleashes too many variables at once. The best approach instead is to identify small, specific, self-contained challenges – then derive data analytics solutions that can address them quickly and on a granular level. There are multiple benefits to this quasi-experimental approach. Firstly, it doesn’t cost a lot. Secondly, it doesn’t disrupt day-to-day operations. Thirdly, if successful, it will create an ethos of positivity around data analytics. Once this strategy has been proven to work on a small scale, it becomes possible to develop an iterative scaleup, extending the approach across the company.
3. Are you getting actionable insights into the right hands?
One of the biggest challenges for companies is getting business intelligence out of silos and into the wider organisation. While the path of least resistance is for the data division to engage in a dialogue with the board, the real analytics superpower is embedded analytics, a capability which allows companies to integrate data into existing processes and workflows so that business intelligence is easily available to whoever needs it, in real-time. A project manager about to make a big presentation to a client should be able to effortlessly access the very latest insights, performance metrics, or status updates on their mobile device minutes before the meeting without having to dig around in emails or on Slack. The data should be in easily digestible nuggets and not require them to log into an intimidating intranet dashboard that they don’t know how to navigate. While that’s fine for data scientists, employees across the company benefit more from actionable insights embedded into their pre-existing workflow. Tools that help companies achieve this include Looker, the business intelligence (BI) and analytics platform from Google Cloud.
4. Is your data strategy designed to be easily digestible?
Creating intelligence out of raw data isn’t only about putting it in front of the right people, it’s also about configuring it in a way that makes the key messages easy to absorb. There are two aspects to this. Firstly, human-centric design. By doubling down on the ‘who, when and why’ of data analytics, it’s possible to create a user experience that amplifies the impact of the business intelligence generated because it’s been designed with the users’ needs front of mind. Often this part of the process is most effective if it is the result of co-creation by data scientists, designers, project managers and people actually using the data.
Secondly, data visualisation has a key role to play in making business intelligence more accessible. A valuable insight to have emerged during the analytics revolution is that the human brain engages more effectively with data presented in the form of maps, graphs, and other illustrative tools. It’s quicker and easier to spot patterns and anomalies using data visualisation tools than it is to deal with a spreadsheet of numbers. Nike uses data visualisation as part of a much broader commitment to data science (it recently acquired its own data analytics firm).
5. Is your company culture ready for data?
For data analytics to live up to its promise, it needs a receptive audience – advocates across the organisation who get the idea and are prepared to back what the data says even if it means short-term pain. So how exactly do you foster a pro-data culture within your business? Firstly, you prove your case – which is where the small controlled data challenges outlined in point 2 come in. The more small wins that these data projects accumulate, the more compelling the case. Secondly, you create a transparent dialogue between data scientists and the rest of the company– perhaps even planting data analysts within departments. The closer the co-operation between data and decision-makers, the less likely there will be walls of resistance or miscommunications.
Finally, deploy data analytics as a way to boost employee wellbeing. By using HR data to identify pockets of illness, stress, low productivity or absenteeism, for example, companies can identify endemic issues early and devise tools to combat them. By showing a commitment via training programmes, seminars, changed working hours and rotating responsibilities, companies can create a culture where employees start to recognise how data analytics can be applied and help benefit their experience of work.
Embracing data analytics isn’t only about making your company function to the best of its abilities. It’s also an opportunity to create innovative new businesses that generate additional revenue streams. A simple example might be a retailer providing suppliers with a new digital offering around weekly competitor sales analysis. This ‘productisation’ of data only becomes possible when a company is confident of surfacing actionable, monetisable insights from its data.
A proactive approach to analytics also heads off the risk that a big tech outside might muscle its way into the picture. Maersk, the world’s largest container shipping company, sees Amazon as one of its key competitors – which explains why it has identified enhanced analytics capabilities as a core priority. With the right handling, data literacy is the best way to stay one step ahead of rivals and preempt industry shifts in a fast-changing digital sphere.
About the Author
Andrew Dunbar is General Manager EMEA of Appnovation, a global digital consultancy which delivers successful business outcomes through strategy and deep industry focus. Appnovation’s clients range from start-ups to Fortune 500, government entities, non-profit organizations and beyond. Andrew is responsible for identifying strategic growth opportunities in Appnovation’s EMEA region and for leading high performing teams in the UK, the Netherlands and Belgium. He’s passionate about solving real world business problems for clients, and helping brands to understand the role digital can play in realising strategic opportunities and objectives. Andrew has over 20 years’ experience leading large scale business transformation in both network and startup environments.