Small Data: use your micro-data to manage your SME
Small Data, Big Data... while we hear a lot about the latter, the former is making its way more discreetly.
Yet the two concepts are not far apart.
Small Data even offers better prospects for the management of VSEs and SMEs.
What's more, according to MyDataModels, a pioneering French start-up in the field, it accounts for 85% of the data collected.
Let's take a look at this promising trend.
What is Small Data?
Small Data: definition and objectives
Small Data represents all the micro-data, or micro-information, collected on a daily basis within a company, via :
- files :
- Excel spreadsheets
- team schedules, deliveries, projects,
- internal studies and reports,
- minutes,
- photo and video files, etc. ;
- applications:
- diaries
- electronic mail,
- instant messages
- social networks ;
- operational software such as CRM (customer databases);
- physical or digital sensors.
Real decision-making tools based on objective, quantifiable and measurable criteria, this data accessible can be used to study and then optimise the productivity and efficiency of all departments, in particular :
- human resources
- sales
- marketing
- logistics, etc.
Small Data vs Big Data
What is the difference between Big Data and Small Data?
With the explosion of the internet, users have never stopped creating digital data (videos, photos, text, etc.), sharing it via different channels and storing it in the cloud.
It is this explosion in volume, but also in the variety of content and the need for speed, that has led researchers to find a new way of storing and analysing this data on a digital basis: this is the phenomenon of Big Data.
It applies mainly to the financial and sales sectors, but also to telecommunications, health, industry, government, etc.
With the development of Artificial Intelligence (AI), large companies, in particular the GAFAMs, and their data scientists can exploit this massive public and private data to guide their strategies.
Small Data, on the other hand, goes against the grain of this "folie de grandeur" trend: sometimes the small amount of data in the possession of the smallest structures can be quite sufficient to improve their performance.
Better still, if properly selected and reduced to a minimum, the predictive analyses derived from it can sometimes be more accurate than those produced by the most complex algorithms.
That's why it's aimed more at business experts who are confronted with a wide range of data but don't know how to use it on a day-to-day basis. We are thinking, for example, of :
- product managers
- marketing managers
- financial managers.
In the following video, Olivier Sibony, professor at HEC, sums up and illustrates this phenomenon very well:
What about Smart Data?
It's not about the data itself, but about the way in which it is used: by concentrating on interesting data, decision-makers are no longer drowning in a mass of useless information, and only use the data that is relevant and usable in the context of a specific issue.
In a way, Smart Data is the result of an initial sorting of data:
- from Big Data
- generated by the company itself, when the data is substantial.
This task is increasingly the responsibility of the Chief Data Officer.
Small Data: examples of applications
Small Data can be used by human resources departments to manage talent or improve quality of life at work (QWL).
For example, ManPower plans to analyse the frequency of use of internal communication tools in order to detect influential employees or any drop in motivation, necessitating the deployment of a retention strategy.
Another case in point: as part of the optimisation of the customer journey, the sales and marketing manager can use the Small Data at their disposal, in particular transactional data such as the average basket and the place of purchase - physical or web - to :
- Understand the sticking points,
- know where to focus their efforts.
How can you make the most of your Small Data?
Analytical tools
The prerequisites for making good use of your data are :
- standardised data collection
- centralising it in a single tool.
Data management and analysis platforms include :
- Data Management Platforms or DMPs, data analysis solutions for collecting, reconciling and unifying customer data, in order to carry out personalised marketing actions;
- dashboards generated by :
- ERP, marketing, CRM and HRIS software,
- Business Intelligence (BI) tools, which connect to all these different data sources,
to collect and monitor changes in a number of performance indicators (KPIs), selected in advance according to your objectives (hello smart data!).
But beyond data analysis, these tools cannot make predictions based on data. Hence the emergence of the following solutions.
Predictive tools
Machine learning can be very useful here.
This concept is based on mathematical and statistical approaches that give computers the ability to learn automatically from collected data, and thus solve tasks autonomously.
With this in mind, MyDataModels has unlocked the technology of Big Data's evolving algorithms to make them usable on a smaller scale.
Even with small datasets, Artificial Intelligence can :
- extract value from data, by transforming your expertise into relevant and interpretable data, simply and directly by business experts;
- produce automatic predictive models to help businesses improve their processes.
The result: the TADA by MyDataModels solution 'makes Small Data talk' to researchers, medical experts and manufacturers, to help them make predictions based on automatic learning models.
Small Data + Big Data?
If you have the resources, using both can be a wise move.
Big Data provides general trends about your sector of activity, consumer habits or the behaviour of your typical customer, while Small Data puts them into perspective with your expertise, your business data and your own experience of the business.
In short, with Small Data, data is exploited through technology, without neglecting the human vision.
Article translated from French