In 2018, no one is seriously asking themselves whether they will see a change in their business from the introduction of advanced analytics – it’s a question of how much change will undoubtedly be the breaking of data silos, to ensure that business skills from various departments are combined. Adaptability is critical for companies. We’ve experienced it being the biggest impact for any client who wants to bridge between their departments.
The key is to blend skills together from data science and engineering to industry support, in order to progress towards the solution the client is trying to build. However, customers often tell us that this approach will cause conflict between departments. On the contrary, we have proven experience that inter-departmental bridging is essential to generating business value in the timeframe that it is expected from the customer.
If companies successfully break down their silos, then they can focus on other more pressing challenges to drive business outcomes. Here are three key challenges for all businesses to consider:
- ‘Insight to production’ timeframes – gathering insight has become a no-brainer for almost all firms, but for a variety of reasons, the time that it takes to turn insight into action is too long. Maybe there’s a lack of appreciation for data engineering or no general framework. This needs to change
- Machine management to speed delivery – still not many companies realise the need for robust machine management. A lot of them employ talent who work with different coding languages, making unification more difficult. There’s a definite need for a general-purpose pipeline management system to ensure seamless delivery of complex projects.
- Effective model management – providing enterprise-wide use case models are consumed by AI applications, for example, but many businesses struggle. Organizations just need to make sure that they are connecting the dots to ensure profitability.
Here are a few examples of how providing a greater insight into enterprise data have changed the way the businesses operate.
Recently, Think Big Analytics worked with a mobile provider that had great visibility of data across the business but had never brought it all together. Our team was able to break these silos with our machine learning models. As a result, the company witnessed how much more efficient they were with data that wasn’t isolated.
The potential benefits of a dynamic analytics initiative are enormous: products can be monetized regarding company’s customer service and asset spending; internal and external communications and oversight can be boosted; profitability of processes can be increased by the application of AI or advanced analytics.
When it comes to making significant, company-wide changes to make for a better relationship with advanced analytics, I’ve got a simple motto – ‘The first step is to take one.’
Eliano Marques, Head of Data Science International at Think Big Analytics. Eliano has successfully lead teams and projects to develop and implement analytics platforms, predictive models, analytics operating models and has supported many businesses making better decisions through the use of data.
Recently, Eliano has been focused in developing analytics solutions for customers around Predictive Asset Maintenance, Customer Path Analytics, Customer Experience Analytics with a focus in Utilities, Telcos and Manufacturing.
Eliano holds a degree in Economics, a MSc in Applied Econometrics and Forecasting and several certifications in Machine Learning and Data Mining.
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