10 Stats On The Challenges Of Data Analysis Today

The sheer amount of data available makes data analysis difficult. Here’s 10 stats on the data analytics landscape today.

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We’ve come a long way since the phrase ‘big data’ entered our vocabulary back in 2005.

Now data is absolutely everywhere, in absolutely every industry. Today, we will generate about 2.5 quintillion bytes of data and, by 2025, it’s predicted that the figure will reach 463 exabytes (that’s the equivalent of 212,765,957 DVDs a day). The rise of ‘big data’ has led to a steep increase in innovation, enhanced customer experience and the rise of groundbreaking technologies like artificial intelligence (AI). And it’s proven over and over again that companies that use data see a huge increase in revenue.

But collecting data is not enough. In order to gain the insights needed to enable data-driven decisions, data analysis is a necessity. Yet with the amount of data available multiplying every few years, data analysis is becoming a more difficult challenge by the day.

Let’s have a look at 10 stats demonstrating the challenges of data analysis today:

1. 65% of companies report having ‘too much data’ to analyse

Almost two-thirds of businesses today are collecting more data than they are able to analyse. This inability to conduct data analysis means actionable insights are being lost. And, rather than finding data analytics a helpful business tool, most companies are overwhelmed.

2. Customer experience: The top needs for improving personalization are: More real-time insights (46%); gathering more customer data (40%); and greater analysis of customer data (38%)

As demand for more personalised customer experiences grows, the challenges of data analysis accelerate. Accessing insights and data analysis remain some of the one of the biggest concerns for those trying to achieve personalization, even today.

3. Almost half of marketing leaders report that more time is spent preparing data to be analysed than actually analysing the data

Although time spent creating ad-hoc reports is down from 2016, data wrangling remains a huge pain point for many marketing analysts today. Time wasted preparing data means there is less time for meaningful data analysis.

4. A third (33%) of data workers say that they waste too much time preparing data

But only one in five (22%) cite a lack of understanding of analytics and data science as an issue.

5.60-73% of data goes unused in analytics today

Data analysis may be a requirement for the deeper, actionable insights that allow firms to stay competitive, but most data collected today won’t be analysed at all. This shocking stat demonstrates just how many missed opportunities for innovation and performance there are every day.

6. Analysing data from marketing sources takes 3.55 hours a week

…Adding up to 280 hours a year spent on analysing data, KPIs and metrics. Because marketers today are frequently working across so many digital channels and using a plethora of tools, the time it takes to conduct data analysis is on the increase.

7. 54 million data workers worldwide spend 44% of their workday on unsuccessful data activities

IDC study reveals that productivity is lost due to analysts juggling an average of four to seven different technologies for their data tasks, as well as the time spent manually updating excel spreadsheets.

8.  Just 15% of business professionals say their organisation is currently ‘very effective’ in delivering a relevant and reliable customer experience

The majority (53%) report being ‘somewhat effective’ and 32% say they are ‘not very effective’ at all. Without effective and consistent data analysis, delivering strong customer experiences to the standard expected today is near-impossible.

9. In the same survey, only 3% of respondents said they are able to act on all of the customer data they collect

A further 21% say they can act on very little of it.

10. 42% of business professionals say that their analytic systems don’t meet current needs

Cisco’s report warns company’s that they can either ‘analyse or die’. Even though it’s not always an easy task, analysing data is critical to staying afloat today.

Key takeaways

Overall, the top three trends we’ve observed in data analysis today are:

1. Organisations are gathering more data than they can analyse – and the first challenge of data analysis is in the preparation of that data
2. The time, resources and manpower currently dedicated to data analysis means that, despite good intentions, huge amounts of data aren’t being analysed and actionable insights are being lost. Data analysis is made more difficult by the fact information comes from a range of sources and is trapped on a number of dashboards.
3. Customer experience and personalization are the biggest challenges that data analysis can help overcome. But, with companies still struggling to analyse their data, this challenge remains significant for many.

How can you cut back on the time spent analysing data?

While the huge influx of data over the past decade has overwhelmed many organisations, it’s also led to the rise of the very technology that’s going to solve many of the problems that data creates: AI.

READ MORE: Data-driven to madness: What to do when there’s ‘too much’ data

Data analysis is tricky for humans. We’re not built to number crunch, and many roles only have a small amount of time allotted for data analysis. We simply don’t have the time to get it all done. But AI specialises in data analysis. The technology is particularly brilliant at constant number crunching and analysing large sets of data, over and over again. This means that it’s perfectly placed to take over the data analytics that most companies are struggling to stay on top of.

Companies using AI to automate data analytics will see benefits including:

• 24/7 analysis revealing anomalies, trends and digital performance opportunities in real time
• Increased efficiency in data analytics tasks
• More free time for employees so they can focus on the innovation and creativity humans excel at
• More reliable results and actionable insights. When it comes to number crunching, humans tend to make mistakes – AI never does
• ‘Analytics-on-demand’. Rather than spending time scanning through data for insights, automated analysis will mean data-driven insights are delivered to you as they happen

Expect to see a significant increase in the number of data-driven businesses using AI tools like our KPI analysis platform to automate data analysis and streamline data-driven decisions.

Looking for an ‘analytics-on-demand’ tool to automate the monitoring of your data KPIs? Why not try out our automated anomaly detection and KPI analysis platform, Millimetric, for free today?

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