Make your numbers add up to something everyone understands.
It’s clear that this is a, if not the, defining decade of data as a business and social change-maker. The data science and analytics fields are lifted by the winds of mature cloud and AI technology. What is now possible to accomplish for leaders in the field is remarkable: T-Mobile Netherlands is boosting its analyst productivity and cutting IT costs with self-service analytics. Data-as-a-service giant Snowflake uses the modern analytics cloud to hit 99 percent of IT commit goals.
Banks are increasing conversion: Northmill, a Nordic neobank with over 400,000 end-users grew customer conversion rates by 30% through better insights driven by self-service analytics, building smarter models to dig into more data, more deeply. And for benefits beyond business, the Modern Milkman grocery delivery service uses analytics to find the customers to whom sustainability and reducing plastics use is a consumer goal.
Startups and major companies alike are pushing to a shared vision creating quantum computing, truly useful and transformative VR and AR use cases, and Web3 technologies for a deeper online experience. Every single advance built on connected technologies will throw yet more fuel and opportunity into the modern data stack and the analytics experience for all users.
There are, and will increasingly be, more and more opportunities to improve ourselves, our organisations, our societies, and our world using data for such causes like sustainability and climate action. There is greater access to intelligence built on data, and companies that think about disruption and opportunity with data at the centre, will be leaders in the new ways of doing business.
This is precisely why organisations not currently leading with advanced analytic programmes must lay the groundwork immediately. It’s vital to build an open foundation for business intelligence to uncover new sources of value. Specifically, in the context of modern analytics, an open foundation refers to the underlying modelling: Not locking in users into particular ways of working that are not effective for their use case. Solutions must be open and integrate with other technologies such as dbt, LookML, SQL, TML, and so on.
Openness isn’t easy, but keeping hold of data without enriching it will not allow for supercharged business activity like creating new revenue streams by building sticky products and applications, for which embedded analytics is key.
But this requires the data and analytics teams to scale hard. New data initiatives mean accessing, sharing, and ingesting the right data at the right speed. That means real-time, Live Analytics, a change from a world of static dashboards and regularly scheduled reports.
Here are some key rules to making the most of the analytics goldrush.
First, good enough does not lead to greatness. Do not be afraid of shopping around for the best solutions to business needs. Managing costs by buying all from one vendor is one strategy, but unless that vendor offers best of breed technology across every solution, then the business is settling for average performance across the board. In a cloud-first world this is not the slow, risky, costly experience that used to fill the CTO and CIO with dread. The modern data stack allows for nimbleness.
Second, everyone is able to add value through insights that will drive better decision-making, so give them the tools that make this easy - real-time, easy, and self-service. Insights are tied to points of view and expertise. Picking tools that only a technical, and therefore, small, group can use is not the way that organisations tap into wider expertise and creativity to really turn the dial.
Self-service analytics, for both the business user and the end consumer by way of embedded analytics within applications, allows more people to ask questions and get data-backed answers. There’s a future coming where we’ll all want to be able to answer questions based on our organisational data without waiting for another team to interpret every aspect for those with the domain knowledge.
Analysts, that is to say, data talent, has an important place, but those skilled experts don’t need to be running queries for others. Put the power in the hands of your users.
Third, make sure those insights drive action. Here’s an unpopular statement in the world data. Analysing data is in and of itself useless, regardless of who is doing it. It’s only if those insights drive action that value is created. Make sure there’s a throughline from insight to action for those at the frontline.
Fourth, flexibility is a strong foundation for growth. There are so many sources of data and growing ways to use them that it would be shortsighted to enforce an inflexible set of policies. Everything changes: Users, their needs, the technology, and types of data. Be prepared to be more organic, more fluid. Stick to good precepts that don’t entrap either your data teams or your business users in a static process. Focus on governance and sensible structures that allow you to evolve as your business inevitably does. Use automation to remove friction so that talent is making magic happen, not banging their heads off out-of-date dashboards.
Fifth, go outside the business to overlay your proprietary data with an outside-in view. Any useful service, like, for example, an online food delivery service, or a real-time route and traffic map, is only useful because it has multiple sources of data. There’s the internal data, and there is the data from partners or users that enrich the service and add the real value.
That’s the restaurant menus, rider availability, weather patterns and user-generated traffic updates in these service examples. It’s the marriage of first- and third-party data. Cloud integrations make access to quality data a breeze, from the Snowflake data Marketplace to Delta Sharing from Databricks, it’s out there ready to be put into use.
And finally, please do not relegate this to an internal exercise. Consider how you bring this to users where they already work, or to customers who want data in their products. Consider data as a part of your user experience. Many systems don’t merely need to be a place where records live. They can be a continually used system offering insights to help users.
This holds true whether we’re talking to an HR system or a CRM. And ideally, we’re talking about an open, intelligent, flexible system that sits above those point solutions and allows users to draw in data from all these places and many more. User experience is important. In the case of the future of our business intelligence, it’s imperative. We cannot make the future if we can’t use the tools intuitively and easily.
By Damien Brophy is Vice President EMEA at ThoughtSpot
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