In today’s data-driven world, your business is sitting on a treasure trove of information. Done right, analytics can reveal insights that drive your business growth, optimise processes and enhance decision-making.
But what if you struggle to translate your analytics into actionable insights?
Are you collecting data, generating reports and investing in analytics and dashboard tools, but failing to gain tangible results?
In latest in our 'Data Engineering and Insights' series, Ian Cockayne explains how you can solve your analytics problems and get them working for your business.
What's stopping your analytics from driving business growth?
Find out what's going wrong, and how you can respond:
1. Lack of clear objectives
One of the most common reasons you might be failing to derive actionable insights from analytics is a lack of clear objectives from the outset. Without a defined purpose aligned to business goals, data analysis becomes a directionless endeavour.
Businesses often collect data because they can - not because they know what they want to achieve with it. This leads to a scenario where vast amounts of data are gathered, but none of it aligns with specific business goals, amounting to wasted resources alongside potentially harbouring compliance issues.
As we laid out in the first article of this series 'From Data to Decisions' , you need to start with a clear understanding of what you want to achieve.
How to fix it:
- Define what success looks like as Key Performance Indicators (KPIs).
- Align your digital analytics to these measures to focus your efforts on extracting insights that have real value.
2. Data Silos and Fragmentation
You may be suffering from the effect of data silos, where data is stored in separate systems or departments and is not easily accessible across the organisation. This can be caused by historical ways of working, or equally by over-implementation of different data and analytics tools, leading to many disconnected data pots.
Fragmentation makes it difficult to get a comprehensive view of your business. Without unified data, patterns and relationships can be overlooked, or may never even be discovered. This will leave you with incomplete data, and a potentially misleading analysis.
How to fix it:
- Break down data silos by integrating across departments and systems. You can do this through centralised data platforms or data lakes, which allow data to be consolidated from various sources into a single repository.
- Invest in data integration tools and ensure data governance policies promote collaboration across departments.
- Create a single source of truth that provides a holistic view of your organisation’s data and enables more accurate and actionable insights.
3. Poor Quality Data
Low quality data is a major obstacle to gaining actionable insights. Inaccurate, incomplete or outdated data can lead to incorrect conclusions, potentially causing you to make poor decisions.
Data quality issues often arise from incorrect data collection, inconsistent data entry, lack of validation processes or use of outdated technology.
How to fix it:
- Establish stringent data governance processes for data collection, validation, cleaning and updating.
- Regularly audit your data implementation and collected data to identify issues and rectify inconsistencies.
- Standardise your data entry practices and invest in tools that can automate data quality management.
- Ensure that data is accurate, complete, and up-to-date, so you can trust the insights you derive from your analytics.
4. Over-reliance on technology
Are you falling into the trap of over relying on analytics tools and technology, expecting them to automatically deliver insights? We all need to be particularly mindful of this, as technology increasingly leverages AI to take the heavy lifting out of data analysis, or decrease the time to insights.
While advanced analytics platforms are powerful, they are only as effective as the human expertise behind them. Google Analytics and Data Studio are merely tools. Without the right analytical skills behind them, you might struggle to interpret their data correctly or fail to ask them the right questions.
How to fix it:
- Invest in building the right skill sets in your team.
- This could include data analysts, data scientists or business intelligence experts, who can not only operate analytics tools but also interpret the data in the context of your business.
- Providing ongoing training and fostering a data-driven culture across your organisation to bridge the gap between technology and actionable insights.
5. Lack of contextual understanding
Analytics often fail to produce actionable insights when data is analysed in isolation, without considering the broader context. For instance, a spike in website traffic may seem like a positive trend, but without understanding the source of the traffic, the quality of the behaviour of that traffic, or the market conditions at the time, it’s difficult to determine if this data point is meaningful.
Analytics need to be contextualised within their environment, not only to validate the outcomes, but also to make them actionable.
How to fix it:
- Incorporate context into your analytics. Don't just look at raw data. Make sure you understand the external and internal factors that influence it.
- Complement quantitative data with qualitative research such as customer feedback, market analysis and competitive intelligence.
- Contextualise data to make more informed decisions and avoid misinterpretation.
6. Complexity over simplicity
Whilst it's important not to analyse data in isolation, be aware of analytics becoming overly complex too. Ensure that you surface data into analytics tools as easily as possible, and present that data in a way that connects easily to your audiences.
When analysis is too complex, it can be challenging to extract clear, straightforward recommendations. Any resulting insights will only lead to more questions, rather than paving the way to actionable decisions.
How to fix it:
- Simplify the analytics process by focusing on the KPIs that align to your business objectives.
- Prioritise quality over quantity when it comes to data and analysis.
- Use visualisation tools to make complex data more digestible by presenting it in a way that highlights key trends and insights.
- Use language that connects with your intended audience to communicate your findings.
- Adopt a hypothesis-driven approach to analytics, where specific questions are asked, and analysis is focused on answering those questions directly.
7. Ignoring predictive analytics
Do you focus on descriptive analytics – what happened in the past – and forget to leverage predictive analytics, which can provide foresight into future trends and behaviours? Whilst descriptive analytics can be valuable, they won't necessarily guide future actions.
Predictive analytics, as described in the second article of this series, ‘Choosing the right analytics’, uses historical data to forecast outcomes and guide proactive decision making.
How to fix it:
- Incorporate predictive analytics into your data strategy.
- This involves using machine learning algorithms and statistical models to predict future outcomes based upon historical data, for example the likely purchasers, top-spenders and churning users audiences in Google Analytics 4.
- By identifying potential opportunities and risks before they occur, you can make more relevant decisions to drive growth and mitigate challenges for specific use cases, unlocking the full potential of your data.
8. Failure to act on insights
So you're successfully extracting insights from you data, but are you acting on them? Hindered by resistance to change? Lacking alignment between departments? Or missing the chance to communicate insights to decision-makers? Whatever the barrier, when insights aren't acted upon, the value of analytics is lost.
How to fix it:
- Foster a culture of data-driven decision-making. Promote the use of data in strategic planning and operational decision-making.
- Communicate insights clearly and tie them to specific actions that can be implemented across the organisation.
- Encouraging collaboration between departments and ensure that insights are shared with the right stakeholders to help drive action.
- Establish feedback loops to monitor the outcomes of decisions based on data insights, enabling continuous improvement.
Conclusion
The roadblocks to actionable insights from analytics often stem from a lack of focus, poor data practices, and insufficient expertise. But don't worry! These challenges can be overcome with the right strategies.
By setting clear objectives, breaking down data silos, ensuring data quality, combining human expertise with technology, contextualising data, and promoting action on insights, you can transform your analytics into a powerful tool for growth and innovation.
The journey from data to decision-making is not always straightforward, but with the right approach, your analytics can become a source of competitive advantage.
Whether you're looking for help overcoming any of these obstacles or simply need practical advice on getting the insight you need from the data you already have - we can help.
Want to make data-driven decision-making a reality?
