In this special blog series, I invite you to join me as I sit down with Cisco’s first Chief Data & Analytics Officer, Pallaw Sharma, to discuss industry trends, fostering innovation, creating value from data, and much more. Have a question? Add it to the comments below and it just might make it into a future post!

Q: Which trends in data and analytics are you most bullish about?

A: Every stage in the data value chain is going through massive transformation right now- from when and how data is generated, to consolidating data into a common platform, to analysis, to visualization, to how and when data’s used- either in an automated manner or served to a business leader at a decision point.

Starting with data generation- through IoT devices, big spots of the world that were previously unchartered are getting lit up. As a society, we’re generating humongous amounts of information while simultaneously learning a lot about ourselves and the users of our solutions- of course, in a compliant manner. This is amazing because the more raw material, data, you have, the more insights you can generate.

Next, there’s tremendous innovation happening in how we connect various data sources. We’re seeing incredibly fast, real-time data pipelines transporting previously disparate data to common platforms where it can be analyzed like never before. Innovations in the data ingestion, pipeline, and storage spaces are occurring everywhere- in the open source, enterprise, and start-up spaces.

Even the tools for underlying business critical needs such as managing your master data, security, privacy and regulatory compliance have massively improved.

The algorithms used in prediction and machine learning have become much more accurate and precise in the last 5-10 years, and I’m not just talking about deep learning and neural networks- the business intelligence algorithms have improved vastly, too. In fact, the analysis we do at Cisco compared to what we used to a handful of years ago is many folds higher.

We’re seeing tremendous innovation throughout the entire data lifecycle- and what’s crucial is to ultimately generate better, deeper insights that’re deployed to the right part of the business- creating a closed loop ecosystem, which we’ve seen more prevalently in the last 5-7 years. To close this loop, you then either connect an insight to a person so they can make a better decision, or you can enable the system to leverage the insight automatically. Using a mixture of these approaches has contributed to data has become the life blood of successful organizations.

In closing, it’s not just about a single innovation such as a new deep learning neural network architecture or a new pipeline. I’m excited about all of the changes across the ecosystem- and at Cisco, we’re at the cusp of leveraging ALL of it.

Q: You’ve increased the value organizations you’ve led have derived from data numerous times in your career. What advice do you have for others looking to do the same?

A: Whether you’re approaching this question from a business or data perspective, what’s most important is a deep, deep focus on the customer and ultimate business value. It’s easy for technical folks, such as data scientists or data engineers, to get laser focused and miss the big picture. Instead, data analytics professionals always need to be asking ourselves, ‘what are the most important elements for the users and the customer to drive business value?’

Oftentimes, a simple algorithm deployed on a large scale generates more value for customers than a complex algorithm or complex framework that can’t be deployed due to system or other limitations. That’s why the first thing we focus on is understanding what problems we’re solving for our customers, our users, and our business.

The second thing to focus on is whether we’re solving these problems at scale. And that’s a very different way of thinking- because you can solve a problem once- but in doing so, neglect to creating capabilities, platforms, and processes that enable the solution to be re-used? When scalability isn’t a priority, it’s very easy to end up with multiple, siloed applications or algorithms which are pretty much doing the same thing- at higher cost to the business and infrastructure.

The third thing is to focus on great talent. If you can attract and retain the best talent, magical things happen- provided you’ve created a collaborative environment, are keeping the customer and business in the forefront, are developing scaled platform solutions, and are clued into innovations.

Fourth, with the ongoing innovation happening all over the world in all stages of the data lifecycle, it’s not humanly impossible for anyone to understand all of it. That’s why it’s critical to have the right people continuously plugged into the innovation ecosystem inside and outside of the company.

Fifth is learning and experimentation. Talented people will seek out the best opportunities then engage in fast, iterative experimentation. This mindset, not think in terms of monolithic, large, multi-year solutions, is key. We need to ask ‘what can we do today and learn from it to do better tomorrow?’

In summary,

  1. Focus on user and customer and business value
  2. Create solutions at scale
  3. Make sure you have the best talent
  4. Get and stay plugged into the innovation ecosystem
  5. Move at a fast pace- learning while iterating rapidly

Stay tuned! We’ll be back with Pallaw soon to discuss best practices he’s developed as a data analytics pioneer.

Have a question for Pallaw?

Add it to the comments below.

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Jennifer Redmon

No Longer wih Cisco