Are you facing pressure to make better decisions, faster? Are you uneasy about making too many gut-level business decisions? Are you being asked to have a data strategy and wondering how to compete in a data-driven world?
You are not alone. These are common themes emerging in today’s digital economy. Customers of all kinds – from consumers to enterprise businesses – have greater and greater choices than ever before. That means your customers are demanding more service, faster, and at a higher quality. How you decide to meet these needs is becoming very complex. You need to choose among many competing options. Increasingly, making these decisions by trusting your gut is a recipe for disaster.
These difficult decisions are not made any easier with the rise of SaaS. While it’s easy to get up and going with SaaS offerings to handle business productivity needs, with every new SaaS offering you use, you end up siloing your data even more. Every department, every business function, has multiple data silos that make holistic business analysis an uphill climb. How can you tie together customer satisfaction and operations data, if the data is in two different systems?
We know this is a common problem, because we hear it over and over again from our customers. We continue to hear about this problem, despite the relative maturity of “big data” systems. If big data has been a thing for at least two decades, why are we still struggling to make sense of it all? Our diagnosis is pretty simple:
- Data projects that lack a business goal will fail, and most data projects lack a clear business goal, such as 'increasing customer satisfaction'
- It's hard to find people to do the hard work of connecting systems and pulling data out
So, despite fantastic big data ecosystems being widely available, if you lack a clear business objective and you can’t assign people to roll up their sleeves and move data to where it needs to be, then unfortunately your data initiative will die on the vine.
Here’s what I recommend:
- Start with a business goal and never put it on the back burner. Listen for and capture business objectives from your team, and hang onto them tightly, while allowing flexibility when it comes to implementation details
- Implement DataOps best-practices. We recommend a serverless option, which would allow you to scale to dozens or hundreds of data sources painlessly
- Connect key data sources out-of-the-gate, such as Salesforce, logs and customer data, or whatever you have identified to support your business use-case
- Finally, make your analytics production-ready and share the good news around your organisation
These are a number of benefits to this approach. Where before you were trusting your gut, now you have real, relevant, current data to support your decision making. You’re not driving blind. Also, since you implemented a best-practices data catalog, you have a crystal-clear picture of how your data got to its end state. You are not questioning “Is this data real?” because you have clear traceability of data from source to metrics.
Also, your analysis gets better with more data sources. Now that you have a central data lake with easy-to-replicate patterns for bringing in new data, you can make your analyses even richer by adding more sources. Many of our customers enrich their data with a wide variety of internal sources, and even external sources like weather and macroeconomic data, to find new correlations and trends that were not possible before.
It’s also beneficial that you’re feeding a culture of DataOps. Word will get around that your team has the ability to drastically simplify data access and analysis because your DataOps foundation comes with commonsense access rules right out of the box. It is not a threat to give access to the right people – it will help your business operate. This tends to have a flywheel effect. Other departments get excited and want to add their data, analyses get better and richer, then even more people want to bring in their data.
To top it off, you are now AI-ready. If all the analytical benefits were not enough, you are now also ready for AI and ML. It’s just not possible to perform any kind of AI with messy data. With DataOps, you have solved two problems at once: you have action-ready business data, and you have cleared the path for repeatable AI projects.
Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.