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Building a Data-driven and Artificial Intelligence Culture in the Enterprise: A Winning Formula

Artificial Intelligence Culture

At Ness, our enterprise customers are eager to explore their hidden treasure in the data that they have generated over the years. Market research shows a growing demand for data monetization, and estimates that the Big Data market will be $125B (USA, including hardware, software and services) by 2025 [Source: Million Insights]. In 2014, Big Data generated $23B in revenue and we are expecting 5x-6X growth by 2025.

Here are additional data facts – According to Forbes, the cloud computing market is increasing with a CAGR of 19% to $162B by 2020. Forbes also reported that 1.5 MB of data is generated every second for every human being on the planet. This means the digital universe of data will stand at about 44 zettabytes in the next few years. That’s an incredible amount of data!

Digital leaders have already started harnessing data generated through systems of record and other semi-structured/unstructured data sources, carrying out exploratory analytics that assist them with business insights and perhaps making more informed decision when it impacts revenue growth and profitability. Examples are Adobe, Nike, Amazon, Google, Yahoo! etc.

One more data point comes from the retail industry which is undergoing severe disruption.  Amazon has been sitting on a data treasure for a very long time. See how they have used to their advantage — today Amazon’s stock price is five times what it was in 2012. Others retail stores are filing for Chapter 11 and restructuring themselves. Why? – Because most of them did not read their customer’s pulse right. Most of the digital laggards among retailers are shutting down stores, reducing their footprint and going back to the drawing board to rework their digital strategy.

CEOs are looking at two simple indexes: How do I increase my revenue share and how do I increase my profitability? For this every CEO is now being asked by their shareholders to evaluate their digital strategy. Three key pillars to drive any CEO’s objectives are: Customer centricity, operational efficiencies and the ability for an organization to innovate by building new products more quickly and more efficiently. How do you achieve this? What are the key ingredients? Of course, DATA! Most enterprises have tons of data which can be aligned to the three pillars of digital transformation which I have outlined. The key is to build that as a culture in your organization.

At Ness, we recently invited some of the industry leaders to discuss how data and AI (Artificial Intelligence) can help them have an edge on their competitors and the key challenges/opportunities they perceive for their organization. We hosted a panel of Digital Leaders from Airbnb, Facebook and Lightspeed Ventures as well as representatives from several other leading organizations, to provide interesting insight on how data and AI are enabling disruption across industries.

Artificial Intelligence Culture_Ness Event

Ness Digital Event

Below I noted the questions we explored with a summary conclusion based on the group’s input:

  • How are analytics used in any industry to create a competitive advantage?

Common Observation: Almost everyone in the panel and the audience agreed that creating a data driven culture is essential to creating a competitive advantage. Enterprises need to innovatively use their data to help them succeed.

  • What are the barriers to entry for a company that wants to adopt best analytic practices?

Common Observation: Access to good data is very critical. This is where data preparation plays a key role. Applying AI on noisy data is not always helpful. Obviously, infrastructure is assumed. The group emphasized the importance of having a properly skilled team to implement data initiatives. Even though big data technology has been in existence for over a decade, enterprises still see a significant scarcity of skill availability to help them scale and overcome their data challenges. This includes selecting the right choices in hardware/software, understanding the use cases and most importantly, having noise free data.

  • What tasks within your industry could be performed by AI? Do you expect to see AI-based products being deployed in the near term?

Common Observation: Everyone agreed that we will see an explosion in AI adoption for repetitive work which machine/computer systems learn and train to execute faster and with higher precision than humans. It will be used to reduce the cost and increase the quality of services. However, scientists and VCs feel AI implementation across the board is still several years away, and replacement of humans is still a myth. For example, analyzing videos to carry out predictive outcomes is still a non-trivial problem to be solved.

  • Are you generally optimistic or pessimistic about the long-term future of Artificial Intelligence and its benefits for your industry? What impact will it have on employment in your organization?

Common Observation: It was agreed that it is difficult to predict benefits and negative impacts in simplistic terms. As the AI usage/implementation matures – there will be government regulations, policies, ethics and so many other factors which will start playing a role.

In conclusion, here are some important highlights and tips to get started with data monetization:

  1. Every enterprise today needs to have a defined data driven story. It needs to come from the top of the organization (CEO, Business Heads etc.). Having a CDO (Chief Data Officer or a Chief Digital Officer) is helpful to define a path to building a data driven organization.
  2. Bring your data into one single place. Do not shy away from bringing siloed organizations under one umbrella and start creating plans to have your data lake/reservoir in place. There are several facets to it and it needs to be executed correctly.
  3. Data Preparation: This in my opinion is the MOST important journey which every enterprise should undertake to harness good data for insights. Good data will differentiate you from others and will provide a significant competitive advantage.
  4. Data Skills: This is a very difficult challenge to solve. Encourage your engineers to explore, learn, and retrain themselves to understand available tools/technologies in the market. Consider working with partners that can provide the relevant skills. You can use them to build your own data engineering center of excellence
  5. Help your business team identify the right use cases that can be solved by technologies.

At Ness, we are here to help you build a data culture for your enterprise. We have experience doing this for several customers across verticals. We can help you too.

I love a quote from Sherlock Holmes (A Study in Scarlett- by Arthur Conan Doyle)- “It is a capital mistake to theorize before one has data”. Do you agree?

Please provide your feedback and feel free to reach out to us.