The great thing about data is that it provides rich insight – and probably most of the answers you’ll need for any given project. The challenge is sifting through data to make sense of it all.
The potential to be buried under a data dump is real – and a continuing battle, without the right tools and mindset. Data science projects require data professionals to devote their energy toward different activities toward project completion.
Looking and searching, rather than the actual analysis, is probably all too familiar for those involved with data. WATS helps to turn the focus around – allowing users to perform the analysis; instead of the search.
Data professionals spend their time involved in different activities during a typical data science project. A survey conducted with ML (machine learning) and DS (data science) professionals asked about how they spend their time during data science projects, including:
- Gathering data
- Cleaning data
- Visualizing data
- Model building/model selection
- Putting model into production
- Finding insights and communicating them to stakeholders
Results of the survey showed that data scientists spend most of their time – about 40% – gathering and cleaning data and the least amount of time putting models into production (9%). WATS provides companies with easy access to data, enabling them to limit the amount of time they search allowing them to spend more time on analysis.
The Big Risk Is The Impact On The Manufacturing Process
Frankly speaking, data collation is tedious and because of that and the tremendous amount of time required to do a thorough job, some companies may skip it. But customers will inevitably notice issues. These may happen down the line. However, having access to data beforehand can help companies fix things, ensuring corrective action prevents an expensive problem heading for production. The big risk is the impact on the manufacturing process and, of course, brand reputation. Complaints can severely damage credibility and the perception of reliability. This in turn can have serious implications for the bottom line.
With WATS this stress is removed. The hard work on data collection is the least favourite part of the engineer’s job. Data sits there. It grows. It’s hard to search in. It can tie you up with inordinate amounts of time; 100 days is not unknown, just figure out the cost in engineer’s time!.
WATS categorises data and makes it available in a way that’s effective, and understandable. We extract the KPIs that companies need. Whilst software will store data, we make it actionable to follow up on quality issues.
Start With The High Level KPIs
Returning to the theme of time spent, let’s look at an example where a consumer electronics company has a hundred products and it took them eight hours to look at one product, they would spend around a hundred days to go through all of the products!
That means users will only be able to identify issues three months down the line. So you could have 12 weeks’ worth of issues but these only emerge on the last day.
Many companies have geared their process from the wrong end. We should start with the high level KPIs (key performance indicators) which is again the first pass yield and it will tell you where to start looking, and then drill down from there, not the other way around.
To get a handle on how to gauge your first true pass yield, check out our explanation here.
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