Unleash the power of cloud insight technologies

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This article was first published by ITProPortal on the 23rd October 2019. Written by CTO, Peter Barker.


‘Big data’ may have been a buzzword across sectors and industries, but only recently has the technology and software needed to unlock its value become more widely available.


Data, and the insight it provides, is disrupting nearly every sector – from manufacturing companies gathering data from sensors to fuel predictive maintenance to Netflix using personalisation to save $1 billion a year in value from customer retention. ‘Big data’ may have been a buzzword across sectors and industries, but only recently has the technology and software needed to unlock its value become more widely available. Peter Barker, CTO at Rufus Leonard shares how you can unleash to power of cloud insight technologies.


Investing in insight

Being an insight-led business requires a lot of data foundation work – intelligence is just the tip of the iceberg. Fortunately, cloud insight technologies have arrived and are finally allowing us to make use of all the ‘big data’ we have been collecting for years, but until now have struggled to use effectively.


One of the greatest challenges businesses face is how to unlock data sets and data points from siloed, legacy systems. Consolidating this data can seem like a mammoth and costly task, but for those that can harness the power of insight, the opportunities are huge.


There are four key steps for defining an insight solution for your business:


1. Sort out your data estate

Bringing together data and assessing its quality is often the hardest step. However, there are now solutions which can ingest and orchestrate data from various sources – be that older systems, applications and increasingly, sensors and devices. This allows us to connect and analyse that data and draw insight to serve into tangible actions. You can now rent fully-connected elastic platforms which do this with relative ease – such as Microsoft Azure’s suite of capabilities and services. Initially we need to orchestrate an ingestion with tools like Azure Data Factory. This allows an automated data integration solution, code free, via a drag and drop UI.


Alternatively, it may be necessary to use the technology to perform knowledge mining, where we can again use cloud services from Azure to perform ‘document cracking’, where we can use document element extraction tools available through the Cognitive Services suite to process data. This can then be stored through one of a number of methods such as a Data Lake storage. This enriched data set is then ready to be used for delivering intelligence and insight relevant to different business use cases.


2. Work out where to activate

Insight itself can be ‘generated’ in a number of ways. Plug in Power BI and start to explore – often how you generate the insight will be based on the specific use case relevant to the business. And this is where machine learning or artificial intelligence tools, which are often forms of pretrained machine learning models, come in. Aligning the use cases you test to your own roadmap for optimisation and growth is essential – whether you’re looking to solve existing challenges, streamline process and operations to save time and money or even find new areas of growth to expand into.


3. Do something with the insight

Most ML and AI models are fundamentally similar, and the availability of pretrained models is increasing. For example, a common use case is understanding a customer’s propensity to churn and identify which kinds of customers will do this. First you’ll need to understand the triggers, model them, test them on a representative data set and then keep iterating.


Now cloud insight technologies don’t replace some key thinking – you still need to apply some data science to your specific data. You have to start with a hypothesis. But using or creating a model can be relatively easy using Azure Machine Learning studio, which is full of easy-to-use tools to help you choose and test the decision tree algorithm and see the success (or not usually to start with) of your hypothesis. So, tweak, iterate and retrain the model and see where you go.


4. Operationalise

Once you have proven the value of the solution, there are a number of tools to operationalise it and allow it to grow, e.g. feeding in more data sets, adding more models, testing new use cases etc.


From here, you can build meaningful and relevant customer experiences driven through insight and intelligence. Utilise machine learning and AI to become predictive about your business roadmap – exploring new areas of growth, testing and building business cases for innovation and optimisation. It’s all about unlocking the value in data and using this insight to power your business’s future.


Start small and grow

There’s no denying that unlocking the power of your data can bring you big business and competitive advantages. It can provide more informed decision making, help you identify new opportunities, drive greater customer relevancy and improve your overall ROI.


While consolidating, analysing and operationalising the value in your data takes time, effort and money, it can be used to streamline and evolve every area of your business. The Azure platform is the perfect environment to do all these things. So start small – rent the necessary software and tools, prioritise business use cases, and prove success and ROI. By building this solid foundation of proven insight, you can grow the estate and capabilities that will ensure your business remains relevant, disruptive and truly competitive in your sector.


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