Harnessing the Value of Data – Practical Applications and Challenges of Data Analysis

Data analytics solutions enable businesses to extract valuable insights and strategic benefits from the information they hold. This empowers businesses to transform an underutilised resource – data – into a precious commodity, one that supports better decision-making, improved operational efficiency and revenue growth.

In our last article, we examined some of the technologies that occupy the data analytics space, and briefly touched upon some of the business benefits that can be realised by implementing these solutions. Now, we’ll consider some of the use cases for data analytics and provide tips on overcoming the challenges that could arise when introducing it to your business.

While there is no strict formula for implementing data analytics, the following settings are particularly suited to its application, and represent the most common use cases:

  • Marketing Analysis. Data is the driving force behind effective marketing. Data analysis can support companies in tracking consumer behavioural trends and quantifying the success of previous strategies, helping marketers develop campaigns that resonate with their target customers.
  • Cyber Security. Modern security solutions, such as extended detection and response platforms, use AI-driven data analysis to spot anomalous activity across different systems and applications, supporting more effective threat detection.
  • Financial Risk Management. Data analysis can be performed against historical financial data, market trends and other external factors, to help businesses identify potential risks and vulnerabilities with greater consistency.
  • Enhanced Manufacturing Efficiency. By combining internet of things (IoT) devices with AI-driven analytics, manufacturing companies can adjust and optimise processes in real-time to drive efficiency savings.
  • Market Forecasting. Data analysis can be applied to historical data, consumer behaviour and economic indicators to help businesses forecast market trajectories, and plan accordingly to maximise revenue.

 

Data Analysis in HR and Recruitment

A key duty of HR, is to evaluate staff productivity both at the level of the individual and the team. So how do you go about creating a clear and communicable picture of productivity performance? You start by gathering data across a number of metrics which might include (depending on the nature of your business) sales outcome data, task completion data, and hours worked vs total output. While each of these measures is valuable in isolation, you want to create a indicator of productivity that amalgamates multiple metrics into a single index, a task that might be time-consuming and complex to perform manually. Fortunately, by leveraging automated data analytics tools you can now amalgamate multiple streams of performance data together, and combine it to create a single, consolidated measure of staff performance that’s able to update in real-time. This same capability can also be applied at the level of the individual, helping HR teams identify the need for further training, and recognise the efforts of top performers.

Systems featuring AI and automation are also delivering operational benefits in the labour-intensive domain of recruitment. For example, applicant tracking systems equipped with AI capabilities are able to pre-screen CVs, helping recruiters to focus in on the best candidates more quickly by filtering out those that don’t meet the job role criteria. AI systems can also play an active role in the selection process by conducting pre-screening interviews, in which candidates are presented with a set of predetermined questions, and their answers assessed and graded by AI-driven analytics technology.

 

Data Analysis in Customer Relationship Management

The information generated and gathered through customer interactions is some of the most important data businesses hold, yet it’s often never analysed for valuable insights.

Data analysis tools and techniques can help you make sense of this data, and create a profile of your average customer. By understanding the characteristics, motivations and desires of the people most likely to buy from your business, you’ll be empowered to develop outreach campaigns that target your most promising prospects.

The time investment required to implement data analysis within your customer relationship channels is highly likely to be regained in the form of better customer satisfaction outcomes and increased revenue. By analysing your customer data, you can develop marketing strategies that deliver reliable results, invest time nurturing your most valuable relationships, and identify relationships that are negatively impacting your profits.

 

Data Analysis in Finance

In the past, accounting and finance roles were strictly procedural, with a focus on record-keeping, the production of financial reports and the submission of tax returns. While these duties remain vital, data analysis is starting to add a new dimension to finance departments, allowing staff to derive insights from accounting data that support better decision-making, effective risk management, and the development of process improvements.

Data analysis can be imposed on historical data to create detailed financial forecasts. These predictive insights can be used to strike a balance between risk and reward across decision-making processes.

 

4 Tips for Conquering Data Analysis Challenges

When implementing change in any area of business, it’s common to encounter challenges and setbacks. This can be true of data analytics, especially if it’s introduced in a hurried, unconsidered manner. Here are some tips to ensure your journey into data analytics goes smoothly:

 

Take Tentative Steps

With significant benefits on offer, you may feel tempted to think big with your implementation of data analytics from the get-go, however this may not be the best course of action. Start by introducing data analysis slowly and incrementally across your business, ensuring that staff understand the technology you’re introducing, and the value of the insights provided.

 

Choose Your Data Sources Carefully

You’ll often hear it said that ‘data is the new currency,’ but while data as a whole can carry immense value, some of the information you hold won’t contain much value in and of itself. Think carefully about the objectives of your data analysis project, and only incorporate data sources that will help you achieve your end goals.

 

Unite Your Data

You may think that because your data is housed across dispersed systems, applications and storage locations that it won’t be possible to amalgamate them to draw out insights and relationships, but this isn’t necessarily the case. Modern data analytics platforms, such as Microsoft’s power BI, support countless integrations, making it easy to merge data from multiple sources, model it to extract insights, and transform raw numbers into attractive and communicable visualisations.

 

A Small Time Investment Goes a Long Way

Investing time in implementing data analytics could pay dividends in the long run, as your business unlocks a myriad of benefits. These could range from resource optimisation and process efficiency enhancements to greater returns on sales and marketing efforts. By unlocking the strategic value hidden within your business data, you’ll empower your business to serve its customers better, allocate capital more wisely and plan for the future armed with data-driven insights.

 

Final Thoughts

Data analytics can seem like a daunting prospect, but with a phased, measured approach and the right technical support, it doesn’t have to be. Keen to venture into the exciting world of data analytics? Get in touch with Datek Solutions today. We can advise on the best solutions for your business’s data environment and your goals, ensuring you get maximum return on your investment.