The Hidden Value in Data – Small challenges, big results

Following on from our previous article where we introduced the ever-expanding scope of data science and its component technologies, let’s now explore some of its practical applications.

While no two organisations are exactly alike, the following are some of the most popular uses for data analytics, AI and machine learning:

• Marketing analytics – track trends in consumer behaviour. Use data to develop intelligent marketing campaigns, in tune with the demands and desires of your customers.
• Enhanced Security – security programmes can gather data from multiple sources and deploy analytics with the aim of creating a more robust security posture.
• Financial risk management.
• Improved manufacturing efficiency – AI-capable IoT (internet of things) networks harness data to provide real-time production efficiency gains.
• Market Forecasting

In HR and Recruitment

Say you wanted to evaluate the individual and team productivity; what are some of the key metrics you might take into account?  Depending on the nature of your organisation you might consider: sales data, task data, hours worked vs total output.  While each of these metrics is easy to understand in isolation, combining these figures into one consolidated performance indicator would be an arduous task to perform manually. Thankfully with the power of automation and data analytics you can now pull multiple streams of performance-related data together to create a coherent, dynamic picture of staff performance.   On an individual basis such technology can be used to spot opportunities for further support and training as well as to reward exceptional performance.

AI and automation are also having a profound effect on the once laborious world of recruitment.  From AI-capable applicant tracking systems which are able to scan CVs to find the best candidates, to AI-powered pre-screening interviews; these emerging technologies have the ability to remove the legwork from the selection process.

In Customer Relationship Management

Customer account data is probably the most valuable information businesses hold, but sadly its value often goes unrealised.  Using data analytics you can bring together customer data to create a profile of your prototypical customer.  This data can then be used to perform data-driven outreach campaigns based on the type of customer you’ve had most success with to date.

By investing a comparatively small amount of time applying data analytics to your customer data you’ll help safeguard valuable relationships, craft marketing strategies driven by real insights and be able to distinguish value-adding relationships from those which are negatively affecting your bottom line.

In Accounting

Traditionally accounting has been seen as a record-keeping role, with the priority being financial reporting and the accurate filing of tax returns.  Now however, tools enable finance teams to tap into and analyse accounting data, providing the insights needed to add value to business decisions, reduce risk and develop procedural improvements.

By exploiting historical data, finance personnel can help steer the future direction of a company by providing the predictive insights needed for low-risk/maximum value decision making.

Overcoming Challenges

Business change rarely comes without a few challenges, and introducing data analytics is no exception.  Here are a few important considerations and tips to ensure the changes you make go smoothly:

Start small

It can be tempting to embrace new technologies with gusto, with the aim of maximum reward in the least amount of time.  It’s best however to start slowly with data science so that your team fully comprehend the new technology they’re working with and the insights it provides.

Don’t incorporate all your data

In our data-rich world with each individual generating an average of 1.7mb of data each second, it can be easy to feel overwhelmed by information.  When introducing data analytics it’s helpful to incorporate information that is meaningful and value-adding whilst omitting the “noise” – data that offers no real benefit or tangible insight.

Combine data sources

If like most businesses, you store data across multiple applications/locations you may think it’s impossible to merge data together to create a meaningful picture of processes, departments and business relationships.  Fortunately though, the tools exist to draw together multiple data sources.  Microsoft’s Power BI platform for example, allows connections to be set up between multiple third-party applications, bringing together previously dispersed data sets to produce visually attractive visualisations.  Leaving data outside the sphere of your data analytics tools can create a misleading or incomplete picture.

Don’t have the time?

Investing a little time to bring data analytics/AI and machine learning capabilities to your business today, could result in potentially massive time savings in the long run.  These time savings will ultimately translate to cost savings, as your team will be available to focus on more lucrative endeavours.

Conclusion

With Universities offering degree courses on Data Science, it’s impossible to condense the vast subject into two short blog articles.  Data science is a daunting prospect, but with our expert technical guidance and assistance we can help power your organisation into the new industrial age with confidence.