There is quite a bit of attention focused on big data, machine learning and artificial intelligence, with these enabling technologies having a significant impact on businesses across the globe.
However, there are some who are still resilient to change and find it difficult to integrate these methodologies and processes into their day-to-day work life. As a result of this, businesses are often confronted with a range of headwinds against these technologies which desperately need to be dispelled if the organisations affected are to thrive in this data-led world.
We’ve broken down some of the most commonly encountered prejudices into four statements frequently heard by business leaders:
1. I don’t need data analytics
“Why would I need to change if my processes are working just fine?”
One of the most common responses heard when discussing the need for analytics is that it isn’t needed.
A quick look at some historical data might dispel this notion. In 1965, the average tenure of an S&P 500 company in the US was 33 years – today, about 50% of the S&P 500 will be replaced over the next 10 years. In the UK it’s a similar story; of the 100 companies in the FTSE 100 in 1984, only 24 were still breathing in 2012.
If companies are to survive, they will need to innovate, and some of the largest innovations we see by successful companies are through the use of data and analytics to drive both top line and bottom line growth.
It is likely that businesses are already leveraging some form of analytics, whether through complex Excel workbooks with commands like VLOOKUP or through more advanced tools and programming languages. The real question is whether they are maximising the full potential of their data and the capabilities of data science.
Technology has moved quickly over the last several years with incredibly simple tools that allow everyone in the business to become more data savvy, and the ability to automate the repetitive data processes that so many companies are still doing manually. Businesses are shifting, and if your company isn’t, you will likely fall victim to the new, shortened half-life of businesses.
2. We do not have the right skills to implement data science
“You have to be a statistical genius or a programming nerd to perform data analytics”
Another common misconception around data science is that those who participate are coders and carry doctorate degrees in statistics and other data science-related fields. This might have been the case 20+ years ago when analytics first hit the business world, but with the ease of use of modern analytic tools, this just isn’t the case anymore.
Self-service data science is aimed at employees who are not professional data experts and provides them with simple to use tools with pre-configured workflows, so they can safely and accurately explore their data and achieve success. This coupled with the online courses that can be taken for low or no cost, and it is difficult to imagine a successful company or employee not working to upskill through the combination of technology tools and training.
Those who are leveraging this accessible software have been coined “citizen data scientists”. These analysts, who are closest to the business problems, frequently know best how to mine valuable insights and drive money savings opportunities throughout their enterprises.
3. We don’t have big data
“I would need a vast amount of data in order to do anything useful with it”
Some believe that in order to derive useful insights you need to have huge amounts of data to work on; however, this is simply not the case. Most data science projects leverage relatively small data sets to deliver significant value, and data scientists frequently are focused on the quality of data more than the size of it.
Accounting and tax offices are some of the first in companies that have recognised the move to digital transformation and leverage data science tools to automate processes and free up their employees to deliver higher value work. These areas typically operate with data that can be held in spreadsheets, and when converted to modern data science tools see time spent manipulating data reduced by orders of magnitude.
Similar transformations are happening in every area of the business world, from logistics to procurement, engineering to legal departments, and each leveraging the core systems and data that their companies have collected for many years.
4. We don’t have time for analytics
“Performing analysis takes up too much time, and I am already staying here too late”
Most employees are already fully absorbed in their current assignments, making the learning of new tools and techniques difficult.
Carving out the necessary time to explore new technology can be a daunting task. Luckily, the learning curve of the latest technology tools is much different than the prior generation. With drag and drop simplicity, built-in help and training, and extensive community support, where other employees that have trekked down a similar path are there to help, the task of upskilling has never been easier.
It is possible to learn these new tools over lunch and implement faster processes the same day. Proof of concept have frequently yielded savings by the end of the first meeting, with the amount of time freed-up greater than the amount of time spent on the PoC.
Does learning take some level of investment? Yes, but the amount of time and money to do this has dropped so low, it is hard to imagine why these new tools are not on everyone’s desks today.
With lower barriers to entry, faster payback than ever before, and the half-life of companies continuing to shrink, the need to provide employees with best-in-class data science tools and training is continuing to grow rapidly.
Whether performing precision marketing to improve sales or analysing data from your customer support operations, from finance to human resources, the world of business is quickly becoming the world of analytics.
The new question is not whether your business is ready for analytics, but whether you have equipped your employees to help your business survive on the analytic battleground.
Verdict deals analysis methodology
This analysis considers only announced and completed artificial intelligence deals from the GlobalData financial deals database and excludes all terminated and rumoured deals. Country and industry are defined according to the headquarters and dominant industry of the target firm. The term ‘acquisition’ refers to both completed deals and those in the bidding stage.
GlobalData tracks real-time data concerning all merger and acquisition, private equity/venture capital and asset transaction activity around the world from thousands of company websites and other reliable sources.
More in-depth reports and analysis on all reported deals are available for subscribers to GlobalData’s deals database.