Once in a generation, the opportunities to create a business legacy increase massively when tectonic shifts quake the business and technology ecosystem.
And with data volumes exploding at a pace of roughly 2.5 quintillion bytes per day, and where in some cases, a company’s data is two or three times more valuable than the company itself, we are quite evidently living in the ‘defining decade of data’.
For a business to lead in this space it must set up the conditions for long-term growth and success. That means building a tech and analytics stack that will take best of breed solutions and future-proof itself for a decade to come. With the tech stack and appropriately robust business processes in place innovation can flourish. Yet time and again companies fail to make good on their business intelligence goals. However, such a state of affairs needn’t repeat any longer.
The mistakes often made along the way
There are a few mistakes that crop up in analytics programmes that hamper building the foundations for greatness. With awareness, project leaders may take a faster route to domination in their data programmes. These mistakes include:
Mistake #1: Falling back on legacy business intelligence technology
Aim for the best in every aspect of the process rather than settling on a good enough solution – particularly if that is code for ‘low cost from our preferred vendor regardless of utility’. That trap simply reduces the ROI on any wider cloud and data investments made. A modern data stack is built for the cloud era and particularly the use of real-time analytics for those making responsive apps and monetising data for new revenue streams.
Again, we are living in the cloud era. Most solutions come ‘as-a-service’. Feel free to swap, change, and experiment, but don’t settle for what worked ‘good enough’ previously.
Mistake #2: Keeping data in the hands of analyst teams
Yes, data must be secure, governed, and regulated properly. But well-governed data needn’t be locked away. You can still empower a wider cohort of business analysts to use it safely. And by business analysts, let’s be clear: Leaders are empowering everyone in the business to integrate their data, bring their expertise, and solve their own problems. Every sales manager, HR director, front-of-house customer service representative, and franchise manager should be able to get answers, test theories, and improve their own service with data, in the moment they think it.
The dashboard is dead in the cloud era. We’re in the real-time world of live analytics. There’s a place for data scientists and dedicated analytics teams, but the ROI for that skilled talent does not come from acting as an asynchronous ‘Google’ for other teammates. Make it easy for the people with domain expertise to answer their own questions as they ask them. Save the experts for where they bring their higher-level skills to bear.
Mistake #3: Making it hard to go from insight to action
Once data gives rise to an a-ha insight there must be a system in place to make change happen. Cloud technologies, once more, come into their own. APIs must connect software together throughout the business so that those who want to act can move from business intelligence to operational systems with no lag. Importing, exporting, and sharing must be simple. Even more importantly, processes and culture must align so that experimentation and action is rewarded when based on data. The right guidelines set employees up to take the right action and encourage innovation.
Make success the path of least resistance!
Mistake #4: Relying on rigid dashboards and data models
The modern analytics stack and real-time data world is down on dashboards. We live in an agile, volatile world, and the ability to experiment, flex, and change is incredibly important. Data can go stale quickly, and dashboards are now merely a way of serving up old news.
The ability to tweak and change is very important. Self-service analytics enabled by search is a great way for domain experts to ask a question, gain an insight, then refine and ask again. They don’t have to be stuck in a process and wait for a data scientist to tweak the parameters. Data has moved from a slow, often monthly-sourced resource, to one that can change on-the-fly, only useful when acted on at pace.
Make solving as natural as questioning.
Mistake #5: Ignoring third-party data
Your data is precious, absolutely, but it’s like having one sportsperson. Only one does not make a winning team – business is not a solo sport. Internal data should be blended with input from third parties to create a stronger proposition. Services that combine first and third-party data become incredibly sticky. Look at Google Maps. They have their mapping data which is augmented by real-time traffic data from road users. Sharing this data means real-time journey planning became viable. Cloud connections make such data integration a breeze.
No one in the new data economy makes it alone. Partner or purchase, and create stronger offerings that offer greater value.
Mistake #6: Neglecting the data user experience
This is a tale as old as time. Stories abound of business failures from poor systems and bad execution. Users are the golden goose – they make magic happen when given the right tools. Solutions must be easy and even joyful to use. All the features that make consumer applications engaging serve to make the front end of the modern data stack a powerful resource, too. Rating, commenting, sharing, saving – the use of data and models can be a social experience. For engagement and adoption think how sticky apps and social media do it.
Avoiding unenforced errors and building programmes using the principles of long-term, real-time, agile, and engaging UX is how an organisation makes a success of the defining decade of data. That’s how a legacy is built.
About the Author
Damien Brophy is Vice President EMEA at ThoughtSpot. ThoughtSpot is the Modern Analytics Cloud company. Our mission is to create a more fact-driven world with the easiest to use analytics platform. With ThoughtSpot, anyone can leverage natural language search and AI to find data insights and tap into the most cutting edge innovations the cloud data ecosystem has to offer.
Featured image: ©Siarhei