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Using the right big data analytics tools can increase Tharsus’ efficiency and productivity. This value is passed on to our customers. And we’re not the only ones.  

As we’ve already explored, the data around data is mind blowing. More data has been created in the past two years than in the history of time. And this growth will remain exponential. As organisations fanatically collect as much data as they can now, based on the assumption that it will become useful in the future, recent studies show that over 99% of it will never be used. The challenge of organising, managing and de-cluttering this data is a huge overhead to many businesses.
 

At Tharsus we see data differently. Yes it has enormous value, but only if it actually delivers something back. 

Let’s take the case of an organisational problem. Rather than starting with data we start with the problem. We challenge hard. Which will be most useful solution to your business? Which to your employees and your customers? What are the outcomes which need to be achieved? What is the value of solving these problems? And will solving these problems cost more money than you stand to make? 

With these questions answered, we begin to define our analytics strategy, identifying the data pools and streams that we’ll use to solve the problem. And we keep sharp focus on these and only these. We don’t allow ourselves to be led into temptation by what seems to be low hanging fruit elsewhere. It is true that analysts sometimes add new data to a project, but they do so in a highly selective way. We know that dumping a mass of unsifted data into the pool only serves to muddy the water.

Here at Tharsus we’re using our big data analytics tools to solve problems of our own. Motion data gathered from our manufacturing teams is optimising the layout of our shop floor. We’re using it to feed a number of different supervised and unsupervised machine learning algorithms to drive improvements in productivity as well as the work environment. Improvements which are delivering real value back to us, and to our customers too.  

We’re also using data to make capital investment decisions. IoT devices streaming real time data on machines and processes, are giving previously unseen insight into the way we work. This helps us to use our current facilities optimally and understand how we meet our needs for the future.
 

Starting with the problem.

UPS collects data from hundreds of sensors in their vehicles. They don’t do this for the sake of it. They do it because they wanted to optimise schedules and make big number savings. Using data they’ve reduced the number of miles they drive by a staggering 5.3 million annually, saving 650,000 gallons of fuel, and decreasing carbon emissions by 6,500 metric tons.

For our customers, who value data driven automation, we bring an understanding of the right data to collect. Not hording as much data as possible in the hope that it will provide insight. Machine learning models are only as good as the data that defines how they have learned. Put simply, you get out what you put in. At Tharsus we’re strategically developing a better understanding of how to define the data you actually need to meet your business organisation’s objectives. No more than that.
 

Data for data’s sake is a dangerous thing. Start with the problem, not the data.

 

Paul Featonby is Head of Data at Tharsus