Customer-Centered Big Data Use Cases

FountainBlue’s February 16 When She Speaks event was on the topic of Customer-Centered Big Data Use Cases.

Delivering personalized solutions to discerning customers real-time will continue to differentiate companies. We were fortunate to have a diverse and experienced panel to help us understand how technologies, companies and leaders are changing the way we work and live.

We began with some definitions – 

• Big Data is a general term referring to the volumes of information made available by the programs, devices, tools and applications we each use every day, in growing proportions. 
• AI or Artificial Intelligence offers a suite of reasonings to draw intelligence from that data, so that it’s understandable and adds value by describing and detailing what’s happening.
• ML or Machine Learning turns to computers to identify and report of patterns which may not be obvious to the average user, and which be useful and insightful.

Our panelists shared a wide range of data use cases which describe well “what happened”, in detail, predicts “what will happen” based on the information provided by volumes of historic data.

Each company has developed sophisticated systems, processes, modules and leaders to help ensure efficient, secure, scalable solutions, despite the complex and overwhelming volumes of data managed, customers served, transactions facilitated. 

Key to providing exceptional service is the ability to anticipate problems, to mitigate risks, to collaborate with internal and external stakeholders in order to anticipate and address needs, and to get it right each time, every time.

Below are some aggregated thought-provoking comments from an expert panel.

• This is a LOT of pressure, considering what’s at stake. But data management is a certain and inevitable direction for ALL businesses in ALL industries. So being open to these challenges and changes will help you keep your skills relevant.
• Partner closely with customers to define, create, anticipate their challenges and needs, and serve their needs efficiently, leveraging real-time data.
• Balance the need for security with the mandate for privacy, and the demand for efficient access.
• Respect the data, but more importantly, use your judgment to ensure that the data provides useful information which is actionable and useful.
• Focus on the prioritized pain points for each class of customer, and work collaboratively to solve them, preferably proactively.
• Data scientists and business leaders are important on each team.
• The hardware, the software, the cloud, all IoT devices add to the volume of data created, and are also instrumental in ensuring we manage the data well.

Our panel ended with some thoughts on the need for humans, for leaders, in an age where data reigns supreme. We will ALWAYS need humans:

• To ask the right questions
• To define the data to be measured
• To understand the implications of the data
• To validate the recommendations of the data
• To take responsibility for the results of a project
• To keep raising the bar, never settling for existing solutions
• To ensure that we are leveraging data for the betterment of all
• To decide what’s ‘useful’ about the data generated, and how it’s useful
• To lobby for the money and energy to fund programs, devices, robots, systems
• To draw conclusions and recommend decisions beyond the synthesized data sets
• To draw creative and intuitive conclusions and recommendations which may not be logical

I’ll conclude this month by inviting everyone to Go Forth with the data, and DO GOOD THINGS.

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Please use us in thanking our panelists for FountainBlue’s February 16 When She Speaks event, on the topic of Customer-Centered Big Data Use Cases and our gracious hosts at eBay.

• Facilitator Linda Holroyd, CEO, FountainBlue
• Panelist Pauline Burke, Global Head of Experimentation, eBay
• Panelist Adriane McFetridge, Director of Engineering, Netflix
• Panelist Maryam Sanglaji, Principal Product Marketing Manager, Nutanix
• Panelist Suruchi Kaushik Sharma, Senior Director, Corporate Strategy, Flex

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Big Data, Machine Learning and AI: Trends and Predictions

FountainBlue’s February 2 VIP roundtable was on the topic of ‘Big Data, Machine Learning and AI: Trends and Predictions’. Please join me in thanking our participating executives and our gracious hosts at Nutanix.

Our executives in attendance represented a range of roles and companies, all with a perspective on how the data, the applications, the solutions, the challenges are impacting our companies and our day-to-day lives. Below is a compilation of ideas and thoughts from our conversation.

We began with the ideal qualities of Big Data: 

Velocity (how quickly the data is moving), 

Virality (how quickly the solutions are adopted and spread), 

Volume (the sheer quantity of data) and 

Veracity (the truth provided by the data).

With the advancement of infrastructures and systems, and with machines and solutions becoming quickly more versatile, more useful, more relevant, companies, leaders, industries are all adopting a wide range of solutions, which benefit everyone across the ecosystem – internal and external to the individual, team, company, geography and industry.

‘Wow’ was the collective response when we heard the wide-ranging use-cases around big data – scenarios which affect ourselves directly and indirectly, scenarios which makes us dream bigger, yet also be more wary about our safety, our privacy, our security, our future.

If machine learning can now help us see trends faster and better than the typical evolved and trained human, then it’s up to us to manage and design solutions to better serve every one of us. We mentioned a few times our concerns about the ethical and human elements surrounding the data – to help ensure that we apply it for the betterment of humankind, our environment, our ecosystem.

The advice and suggestions collectively as a group are summarized below.

• Choose open source as a foundation for growing solutions and offerings to target markets. Actively participate in the open source community to give back, to influence the direction, to expand and create collaborative networks.
• There’s value in the data, and winning companies will learn how to monetize on it, while also respecting the privacy and rights of the individuals who own the data.
  • Not all data is treated equal, so categorizing into data types will help build a standard and help respect the privacy of the users, the intentions of the solutions.
• In this digital economy, data is the currency. Ensure Access, Security, Reliability, Speed, Versatility, Accuracy, etc., of same. This is not an easy task with the 4 Vs of data highlighted above.
• Privacy, Security and Access will be consistent challenges and themes. 
• A focus on customers and their demands for personalized access to data real-time provides a challenge and an opportunity in all industry sectors.
• Heavily-regulated industries including healthcare and finance provide specific niche opportunities around the data due to the regulations and policies and lack of standards for the industries.

Highlighted opportunities and challenges are listed below.

• There will be a battle around standardization, data location – Edge vs 5G for example, 
• Balancing privacy, security and access
• Selling your own usage data
• Leveraging automation and robotics to better perform – more precision, more dexterity, less tremor, better access, use lighting and imaging
• Leveraging data for diagnostics will add value across industries
• Analyze trends to better predict and serve customers, to more strategic invest in ideas and companies
• Understanding usage, sentiments, trends and tendencies is a huge opportunity and will only get bigger. 
• With successful understanding of a broader range and larger volume of data real-time, there are opportunities to decrease churn, increase revenues, increase positive resolutions, increase Net Promoter scores, increase customer loyalty and referrals, etc.,

But the human will also be necessary.

• As a sanity check for the data.
• To program the HW/SW solutions and identify what’s relevant, what’s actionable, what’s valuable.
• To provide feedback and intelligent guidance to automated scripts.
• See beyond the data and its implications to imagine or extrapolate a trend or idea.

The bottom line is that Data and Content are in charge and affecting each of us across roles, industries, geographies and scenarios and collaboration is key. Energy and technology will help ensure the safe, secure real-time access to data which is actionable. Everything will be different, and yet the same, perhaps at a different scale.