These days, thanks to new technologies, the amount of computer data being collected is growing rapidly while simultaneously becoming more complex. The data revolution is the driving force of our time. First, we went from “data” to “big data”. Now we are talking about “full” data. What will be next?
Before going into the details, let me pose two questions to illustrate the usefulness of collected data:
(French text: What is the link between babies and beer? Why are Google services free?)
This is not the answer. Beer is not for babies!
No, the link between beer and babies was found in some studies that demonstrated:
➢Beer sales are particularly important on Fridays in the early evening;
➢Customers who buy beer during this period also tend to buy … diapers!
By placing these two products close to one another, grocery stores increase their sales and profits (cross-promotion).
The Google principle:
Acquiring information usually has a price, but Google uses its services to acquire free data from its users.
➢Analysis of the text of emails (Gmail);
➢Profile and contact list (Google Groups);
➢Schedule (Google Calendar); – etc.
This information is used to send users targeted advertising.
Nowadays, possessing data is a critical competitive advantage. Data tools archive customer profiles, buyer’s behaviours, team performance, and many other aspects of information that wouldn’t necessarily be intuitive, but are vital in the development of marketing strategies and long-term company growth. The more data we have, the better we will know what actions to take for our business.
However, the colossal amount of data can be overwhelming and confusing, and the attempt to make sense of it can often prove a fruitless endeavour. To make matters worse, data is more or less useless when not managed or interpreted properly.
“Companies that manage their data as a strategic resource and invest in quality are ahead of their competitors in terms of reputation and profitability.”– PricewaterhouseCoopers Global Data Management Survey (2001)
First of all, we have to grasp the differences between data, information, and knowledge. Data becomes productive only when it has been processed and presented in a way that heightens our understanding; it has truly served its purpose when it leads to specific courses of action based on that new understanding.
DATA | We are talking about data when we are dealing with a series of raw value elements that can be processed, calculated, or measured. However, they may not mean much intuitively because they are not presented in context.
INFORMATION | We can obtain information only when data is intentionally organized to highlight the relationships between the different elements. Through this organization, raw data is given context and meaning. At this stage, we are approaching knowledge.
KNOWLEDGE | To begin to understand our data, we need to turn it into information in order to more closely analyze it and make conclusions. By understanding this information, we gain insight, or knowledge that will help us make the right decision. Knowledge, then, is understanding the data, the key that will guide us in the decision-making process.
For an alternative presentation of these ideas, see the chart below:
Having understood the real-world benefits and limitations of data, one may wonder, is merely collecting heaps of data enough to help a company make a decision?
Moving from data to business decision-making is the result of an increasingly complex process; the amount of data to be taken into account can be overwhelming, and the human and financial stakes are often so high that the technology for processing such information has necessarily become incredibly strategic.
BI (business intelligence)is the ability to analyze data. Thanks to artificial intelligence, users are able to utilize the available information in order to improve their decision-making. This ability to analyze information is particularly essential for managers, enabling them to better understand their environment and, from there, be more strategic when planning courses of action for their company.
However, it is not easy to choose the right tools for analysis and, in turn, decision-support, as there are increasingly more data exploration tools available. These data exploration tools are not helpful in decision-making.
To better understand the difference between an data exploration tool and a decision-making tool, I’ve developed this analogy.
Imagine that you want to write a story, and you have the choice of being aided by either a dictionary or a manual that instructs you specifically on how to write.
Your goal is to use the most appropriate tool to help you write your story, but also the tool that will allow you to write your story as quickly as possible. In order to be able to choose the right tool, you must analyze the characteristics of each.
Though a dictionary would give you an incredibly in-depth understanding of what you were writing, can you imagine the time it would take to look up each word, compared to the speed with which you would write the same story with the help of a specialized manual instead?
My advice: don’t get lost in an unnecessarily complex dictionary that does not actually give you practical advice on how to write your story. Read a simple and clear manual to guide you to the path that will have you reach your goals as quickly as possible.
In conclusion, you do not need a tool that will make you an expert analyst of your data. Constantly performing complicated in-depth analyses of your data is unrealistic, and impossibly impractical courses of action do nothing to help you reduce the risk of your business.
Good decisions are no longer good decisions when they arrive late and the deadline for acting has passed. You need to make sure your data speaks to you in real time, when you need it to; only now can you achieve your goals and ensure the sustainability of your business and plan for the future.
What will you choose? Dictionary or manual?
A study by Deloitte shows that data analysis is the key factor in making better decisions (49%). See image below.
The same study also shows that 49% of companies say that the technology they currently use to make their sales forecasts is not sophisticated enough to provide predictive information. See image below.
The complexity of information and resulting changes in practice are developing with increasing speed, due to the incredible pace of technological advances. It is therefore necessary to adapt quickly and equip ourselves with tools that will enable us to make better decisions.
We now understand that we need a set of technologies and tools to transform:
1) Data into information;
2) Information into knowledge;
3) Knowledge into strategies that lead to better decisions for our companies.
This first step in the commercial use of data is complemented by software capable of making recommendations and suggesting decisions based on that data. This is called prescriptive analytics.
Below is an important schema that perfectly describes the long road that leads to a discerning application of AI in business.
I hope this article has been informative and helps you better understand why skillful data management is critical to your business.
However, as life is made of decisions, having to decide which decision-making tool suits your company best is another puzzle in itself.
I will go deeper into the various decision-support tools in a future article.
Feel free to share any thoughts that may enrich this article. It would be my pleasure to hear what you have to say.
Virginia Ene | firstname.lastname@example.org
(Translated by Courtney Hinz)