Opening The Door To Innovation With Data Superpowers
Source:-forbes.com
Helping your data teams do their jobs means empowering them to work efficiently and without barriers. This article looks at how AI and ML technologies can empower your teams to help your business grow, and how to make modern data analytics work for you.
Last week, I celebrated my first anniversary as general manager and VP of engineering for our data analytics platform here at Google Cloud. Despite all the unprecedented experiences we’ve had this year, we’re thankful to have technology that allows us to continue to have strong, direct relationships with our customers. No matter the industry—finance, retail, gaming, or telco—people often ask me how they can run their businesses like Google. What they’re really asking is how they can apply the latest innovations in data analytics and AI/ML toward better business outcomes, and whether the secret is in hiring the best and the brightest minds.
As the World Economic Forum highlighted a few weeks ago, data has become a “new factor of production” that has the potential to change business models, push industry boundaries and create new market structures. I’m humbled by the fact that so many customers have chosen Google Cloud to help them on their journey to innovating and transforming their business with data. What I’ve found in my years of experience is that what really matters is enabling your teams to do their best jobs.
Today, with the incredible technologies we have available, helping your data teams do their jobs means empowering them to work efficiently and without barriers. There’s so much possibility with AI and ML technology, along with real-time analytics, but there’s also so much to know and understand about how these technologies work, and how you can use them wisely within your organization. And having the right tools opens doors.
Empowering your teams to do their best work
Today, users in every area of the business—sales, marketing, logistics and others—need to access and use data insights to do their jobs. But they often have to rely on a handful of data analysts to get that information. When it comes to more sophisticated analytics, like making predictions, even data analysts are not enough. For that, business users have to lean on data scientists, who are even more scarce. And everyone relies on data engineers to make sure that data ingestions and processing machinery are up and running.
When each of these users is empowered with the right tools, they can do their best work. That’s a leap that many businesses can make today—the solutions are available, and data experts are eager to dive in. Of course, in today’s broad data landscape, it can seem overwhelming to know where to start with data analytics tools. For many businesses, it still seems like a huge leap to incorporate specialized technologies such as AI and ML. Until recently, using these advanced tools required skills or intensive training, or a big financial investment. But restricting advanced tools to a select few in a business just isn’t scalable, and it doesn’t lead to productive teams, either.
Open, intelligent data platforms that bring modern analytics are essential right now, especially as businesses recover and reset after disruption.
How can you empower your teams to help your business grow? To address our own challenges of scalability at Google, we had to build tools ourselves to run the business. Now, those tools are available for our Google Cloud customers, and our goal is to empower users everywhere with our technology like we do with internal teams. You can bring advanced analytics tools to your users, so they don’t have to depend on their data analysts and data scientist counterparts to get the information they need. Data scientists and data analysts can then do higher-value work that has more of an impact on new product development and other big initiatives or projects.
Open, intelligent data platforms that bring modern analytics are essential right now, especially as businesses recover and reset after disruption. Streaming analytics and AI let people plan for the future. Circumstances like the COVID-19 pandemic, with quickly changing customer needs, show that real-time and predictive capabilities are vital to a business.
While moving your business toward a data-driven model might seem like a huge task, it’s definitely possible. I’ve seen so many examples of company and team leaders taking the leap, and so many success stories over the past year (KeyBank, for one). Adopting modern technology leads to changes—not just in processes or formats, but in how teams work both independently and as a unit.
How to make modern data analytics work for you
Two men opening warehouse doors
With data analysts able to get what they need on their own from their data warehouse, data engineers can focus on strategic work that creates value. GETTY
As you consider how to democratize the availability and use of data and empower teams to do their best work, the model of Google Cloud’s smart analytics platform can offer a glimpse at a successful setup. With your data in BigQuery, you can access and analyze it in different ways. Our BigQuery interface uses standard SQL, so data analysts can use their existing skills and knowledge to write queries and create reports, interactive dashboards and more. And data analysts can manage pipelines and load data themselves, plus train and run AI and ML models using SQL with BigQuery ML, so they’re less reliant on data engineers or data scientists.
For those without SQL expertise, BigQuery offers integration with a spreadsheet interface with Connected Sheets. And as we bring more innovation with Data QnA, our natural language interface for your data, anyone can simply ask questions of their data, no expertise or reliance on data analysts needed. Finally, for data scientists who want to continue using their notebook environments to analyze data, we offer APIs that allow them to use Spark, Pandas, Presto, and more on their data in BigQuery.
With data analysts able to get what they need on their own from their data warehouse, data engineers can focus on strategic work that creates value, like managing data quality and lineage, rather than spending time making data available to data analysts and business users (like many engineers do now). And data scientists can focus on creating more sophisticated and robust models and operationalizing them for the business.
This week is data analytics week for Google Cloud Next: OnAir, so I invite you to tune into my keynote, where I share more about our guiding principles in data analytics development for Google Cloud and our vision to offer an analytics platform with proven dependability for mission-critical workloads that is open, intelligent and flexible–all essential to building and planning for the future. When realized, the result is an organization where everyone who needs data is empowered to use it in the way that works for them. That’s when the door opens up for innovation and thinking big. Here’s to many more years of asking questions and putting the answers to work for you, your teams, and your customers.
Keep reading: Check out this HBR report to hear how today’s leaders are turning data into unmatched business value.