(US & Canada) VIDEO | Take the Offensive Route and Create Data Foundations Today — Google Global Solutions Value Practice Leader

(US and Canada) Monisha Deshpande, Global Director of GoogleCloud Value Advisors, and Joyeeta Banerjee, Global Solutions Value Practice Leader, Data Analytics and Security Solutions at Google, speak with Derek Strauss, Chairman of Gavroshe, in a video interview about having solid data foundations for business KPIs, data ecosystems, data democratization and monetization, generative AI, and developing a data-driven culture.

At the onset, Banerjee states that business leaders faced many pressing challenges in 2020 and 2021 due to disruptions caused by the global pandemic. She discusses changing consumers - when people were at home versus when they were back at the workplace. Banerjee recalls that the key struggle for business leaders was gaining supply chain visibility.

Next, she emphasizes the need for an accurate prediction ability for businesses to have the right product in the right place, at the right time, at the right price, and for the right consumer segment. Banerjee stresses the importance of having a solid data foundation to make the process a reality, and then applying this framework to the various business aspects across the value chain. This way, she says, businesses will get the desired KPIs and business impact.

In addition, Banerjee states that data is only as valuable as people who get insights from it. She reveals that 68% of organizations cannot realize tangible, measurable value from their data. To counter this, Banerjee emphasizes identifying critical personas across the data platform.

Delving deeper, she discusses three prime personas:

1. Data Engineers
2. Data Scientists
3. Business Stakeholders

Banerjee explains that the role of the data engineer is to manage and convert raw data into a usable format for data scientists, analysts, and business users. Further, she mentions that data scientists spend 38% of their time on data preparation and only 29% on reporting, presentation, and visualization. She then describes how leveraging pre-built AI/ML models can shift power to data scientists.

Similarly, Banerjee emphasizes that business stakeholders must have data responsive to real-time events so they can take action quickly. She notes that the internal data ecosystem comprises engineers, scientists, analysts, and stakeholders.

Moving forward, she notes that external data sharing, or data democratization, is becoming a very core concept. After investing heavily in this, she reckons that in a retail use case, the more the providers are empowered with data, the more processes will be streamlined.

Banerjee also mentions that data monetization, or creating new revenue streams from publicly shared data, is fundamental in data democratization. She encourages data leaders to leverage data solutions to empower, both internally with people and externally with partners and customers, to drive the desired outcomes.

Taking over from Banerjee, Deshpande sheds light on leveraging generative AI. She states that for organizations to take advantage of generative AI, they must ensure real-time access to their private corpus of data, ERP systems, and other data. Furthermore, data leaders must be able to extract complex data and feed them back to the business.

A modernized data platform and advanced organizational practice around MLOps are also necessary for connecting all the dots, she adds. Deshpande hypothesizes that the true power of generative AI and AI comes from combining the two, which allows businesses to optimize technology costs while increasing returns.

Continuing further, she emphasizes the importance of testing and learning to maximize gains. She mentions how generative AI can develop 30 highly customized ads, and then traditional marketing can optimize or recommend AI solutions to create the rest of the ads more economically. To do this, Deshpande suggests having a robust data platform that is easily accessible, with access to ML data sets.

When asked about the future of data, Banerjee wishes for a day when data can address world hunger, climate change, and the education crisis. Despite the current macroeconomic conditions, she advises data leaders to take an offensive route and make strategic investments to unlock value and create a data foundation that can lead to future revenue growth.

Thereafter, Deshpande expresses that a strong data foundation leads to success in AI. She believes that one critical success factor for enterprise AI is ensuring that data management is optimized to enable data-sharing, discovery, and reusability.

In conclusion, she stresses the importance of developing a data-driven culture comfortable with failing fast and failing forward. These two components can ensure that an organization unlocks its full potential with AI, says Deshpande.

CDO magazine appreciates Monisha Deshpande and Joyeeta Banerjee for sharing their valuable insights and data success stories with our global community.

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