Data gravity expanded and relied upon

decision

This is a series of blog posts on the exploration of data gravity as introduced by the most recent DXC TechTalk. You are now reading part 3. Previous posts: 1, 2

As we go into a new era beyond the era of convergence, data will be key. The importance of accurate, timely and precise data will be crucial in a world of 24/7 data sensing. Simulated data (not real) and actual data (real data) will divide the world of technology. Both types of data will be useful for simulations, optimizations, predictions and artificial intelligence. What will be crucial is that we consider the data gravity of the real or the not real data.

I personally believe that with simulated data, it is very difficult to predict what the data would do in certain environments. Where possible, we should consider the effects on the model that we are creating and the environment in which the data is located. My personal preference is that wherever there is real data, let’s use it to understand what is happening. However, real data does not always provide the extremes that a simulated data set provides. So, often using both is your best option.

The nature of data gravity is that it keeps changing. Yet, how quickly it changes is unknown. The more data there is, the harder it becomes to move the data, so the gravitational pulls and pushes upon that data are going to become harder and harder to move and shift. Gravity does change over time as we get older and perhaps live in space or underwater. Have you ever tried building a data centre underwater? I know people who have. Extreme environments are the best places to test technology and the data associated with the technology.

As data becomes an even more valuable asset, data gravity is going to become even more useful in assessing your data. However, I propose we need to consider the environment where your data will be located and collected. The cloud is great, but in the future we may have data in very different environments such as Mars or other planets, the desert or the sea.

We have to think differently about data. Do you know how annoying it is when your weather station goes down (stops collecting data) because the cows love the weather station? The environment the data is in is crucial — both where it is collected and where it is stored.

data gravity 2#DataGravity for 2017 and onwards

Data gravity has new meaning in the world when executive leadership is changing and the roles of CXOs are changing rapidly. It is more important than ever to think about data gravity. CXOs rely heavily on the data that is provided and the analysis that is undertaken on that data. The gravitas of the data in any situation is extremely important to the problem it is trying to resolve.

Data is what joins CXOs and leadership teams in an organisation. Without understanding data gravity, it can be very hard to make decisions using the data provided.

The CXOs of the future have already thought about data gravity and the effect on their organisation and people. How will data gravity affect your organisation? We would love to hear what you think and if we can help.

Feel free to leave a comment below, or engage me in a conversation on Twitter at @GeoSuperGirl and use #DXCTechTalk. Let’s get the community conversation started around #DataGravity.

To keep stretching your brain, join the next DXC TechTalk on July 26: Digital Goes Back Office: Continuous Optimization of Enterprise Processes. See you there!


Sarah James was ANZ lead for Authentic Leadership in DXC and an advocate for DXC’s Women in Leadership and STEM. Prior to leaving DXC in September 2017, Sarah founded the Empowering Future Leaders blog and was its primary author. With over 15 years of experience in the world of IT, Sarah’s specialty is spatial information and includes integration on projects as diverse as mapping volcanoes in Hawaii to delivering high-tech police vehicles.

RELATED LINKS

Data gravity: The things you never knew you never knew

Data gravity defined and equated

Transforming to a digital enterprise

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