AAA SVB: Capturing the big promise of big data

SVB: Capturing the big promise of big data

 

We are all searching for ways to use big data to identify new products and customers, push the efficiency envelope and drive to better bottom lines. But if big numbers scare you, you are in for a fright – IBM says we create 2.5 quintillion bytes of data every day, and that 90% of all the data in the world has been created in the past two years.  See: www-01.ibm.com/software/data/bigdata/what-is-big-data.html
 
Big data analytics is being used to predict power outages, combat cyber-criminals and trace outbreaks of deadly diseaseand less urgent pursuits such as finding lost airline baggage. By having even a rough indication of a cellphone location, collected when a call is made or received, researchers get invaluable data on mobility patterns to target resources inthe areas of greatest need. This technique has been used in malaria and cholera outbreaks. See: www.economist.com/news/leaders/21627623-mobile-phone-records-are-invaluable-tool-combat-ebola-they-should-be-made-available

We recently teamed up with Citi Ventures to bring together corporate venture arms, start ups and traditional venture capitalists to talk not just about the promise of big data but more importantly about the opportunities and challenges around delivering on that promise.

We are in the very early stage of big data. As the internet of things gets going, the challenges of how to process, manage and use the data will grow exponentially. And what is now crystal clear to me is that before we can figure out what to do with the data, we need more focus on how to clean the data so it is usable and relevant.

One of the biggest opportunities for start ups is helping enterprises find more cost-effective ways to process data faster. Focusing on data processing is becoming a bigger priority for many companies, and these corporates are often looking to start ups to provide at least some of the research and development.

Finding the big promise in big data is complicated. Generally, the ecosystem lacks the appropriate materials and tools that data scientists need to make the best use of the information. There are lots of companies providing data analytics, but not enough solving the problem of finding, profiling and cleansing the right data. It often requires too much time for enterprises to identify and prepare data to make it useful.
 
Where problems persist, opportunities exist. Enterprises seek agile start up partners to help them incorporate new technology into an existing infrastructure. Today, there lacks a unifying middle fabric to connect data providers to consumers of data effectively, fusing management of data, and minimising movement of data into one process flow. Many think this likely will be an open-source project backed by a large enterprise.

Some of the projects discussed by corporates were:

  • AT&T has created an open-source platform so entrepreneurs can access and transport more data.
  • SAP has a programme in place to guide start ups through their client’s organisations.
  • Citi partners start ups closely to evolve their enterprise capability set to include key cutting-edge features in the areas of data security, access control, compliance and governance which are absolutely essential for large-scale deployments. Citi Ventures has a team dedicated to commercialising companies both inside and outside its portfolio.

Take aways from the investor community suggest they are focusing on:

  • Data cleansing solutions.
  • Self-service analytics to allow for decreased reliance on highly trained data scientists.
  • Improved data securitisation and protocols to protect data.
  • Solutions that use new machine learning models that remove the need for steep human learning curves.

Enterprises also learn a great deal from engaging with start ups, and some highlights of what they look for in pitches are:

  • Conversations about business problems at hand and the underlying process problems that hinder the solution.
  • Enterprises have lots of new assets and want to give intellectual property to companies that will do something new with it. If start ups have not conducted pilots, then enterprises are not easily convinced the solution will work.
  • Alignment on business model – a start up should make money when the enterprise partner makes money to keep the relationship balance.
The best advice I heard – make sure there is real understanding of the goals and capabilities on both sides of the table. Of course, these lessons apply to all kinds of new ventures setting out to explore a vast new territory. At the same time, I was encouraged by the clear-eyed optimism, and I am confident we will see amazing results from big data. I hear AT&T is working on a solution so a lost airline bag can send its location via a text to its owner. See mashable.com/2014/05/18/
att-innovation-showcase/

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