“The term Big Data refers to data sets which are too large, too complex, too fast-moving or too weakly structured to be evaluated with manual and conventional methods of data processing. Big Data is often used as a collective term for digital technologies, which are held responsible in technical terms for a new era of digital communication and processing and in social terms for a social upheaval. The term as a buzzword is subject to continuous change; it is often used as a supplement to describe the complex of technologies used to collect and evaluate these amounts of data”. This is how Wikipedia defines Big Data.¹

Google lists in 0.43 seconds “about 6,440,000,000 results” of Big Data. Big Data is big in the truest sense of the word. It is an international market with global players. It’s a promise of transparency. Transparency about customers and users, about motives and maybe even about “hidden” wishes. And that’s great, isn’t it? Everyone wins: the companies that understand their customers better and generate additional sales. The data analysts and manufacturers who are pleased about numerous companies as clients. And the end customers and users who receive what they have always really really wanted.

Do you already use Big Data in your company? If not, I have a suggestion for you: How about “Small Data”? The good thing about it is that you already have everything you need today.

Small Data, Big Data or Smart Data

What’s small data? Wikipedia can also help here: “Small data is data that is ‘small’ enough for human comprehension. The term ‘big data’ is about machines and ‘small data’ is about people. This is to say that eyewitness observations or five pieces of related data could be small data. Small data is what we used to think of as data. The only way to comprehend Big data is to reduce the data into small, visually-appealing objects representing various aspects of large data sets. Big Data is all about finding correlations, but Small Data is all about finding the causation, the reason why.”²

Aha. Big Data is for machines, Small Data is for people. Learning never stops. I thought Big Data would be analysed and evaluated with “machines”, but it would also be something for people. It’s really easy to get defintions wrong. And what is “Smart Data”?

Again Wikipedia has an answer: “Smart data are data sets that have been extracted from larger amounts of data (see Big Data) using algorithms according to certain structures and receive meaningful information. This data has already been collected, sorted and analysed and prepared for the end consumer. The data must also be understood by the user in order to achieve a meaningful result. Smart Data can be used both to gain new insights using raw data and to create models that can be used to analyse data.”³ If I understand it correctly, Smart Data is about preparing the analysis of Big Data. By machine and/or man.

Personally, I would be happy if both Big Data and Small Data were processed smartly. I don’t know what the opposite of Smart Data could be; a non-smart processing or evaluation? That doesn’t make much sense. I also doubt that the term “Smart Data” is a hidden criticism of the collecting frenzy of companies: Collecting data in order to collect it and to extract clever insights with a machine in the future – who does such a thing? Except …

Small Data for your customers

What’s my point? I cannot tell you whether an investment in a Big Data solution and an analysis of perceived infinite data sets is worthwhile for you and your company. But I know that often no “magic” linking of data sets by an expensive machine is necessary. With little effort you can gain ideas for your own actions and effects. The only thing you need for this is a clear focus, the will to improve and access to selected information that is already slumbering in your systems today.

Here is an example:

  • How long does a customer wait in the waiting loop of your hotline?
  • How often does the customer “have to” call you on the hotline before he can talk to someone about his problem?
  • How often is your hotline unavailable during the contractually agreed times?
  • How many customers can your service employee “support” at the same time in the chat?

Many companies “optimise” their own support in terms of capacity utilisation. Evil tongues would perhaps even say: in the direction of overload. Very few companies optimise in the direction of their customers so that they receive the best possible support. And this point addresses only the accessibility, and not the actual problem treatment. In other words: the time factor.

I tell you a secret: very few people like to wait. Not in a cafe for the coffee ordered 15 minutes ago, not in a car dealership for a sales consultant and not at your hotline for 25 minutes. Few people like to look for salespeople who hide rather than volunteer to help. Or wait 6 weeks for a new sofa. Good or even very good service as a competitive factor seems to be something completely unknown to many organisations.

Of course there are many more examples:

  • What happens in your company with customer feedback?
  • How do you react to inquiries, applications, invitations, cooperation requests or offers from other companies that do not meet your requirements?
  • How often do you postpone the delivery of updates or upgrades even though they have been communicated?
  • How many systems in your company store customer information?
  • How many of your webshop visitors cancel their visit even though products are in the shopping cart?

Probably your answers already offer enough optimisation potential. I would like to give a brief example of one of the examples: customer feedback. Perhaps you offer customers the opportunity to give structured feedback in your organisation. In the form of a small scale or with smileys on your website or by questionnaire at the end of an event.  Surely you know by your private Internet use the meanwhile relatively usual questions à la “How satisfied were you with …”, “How do you rate …” or “Would you recommend us?” Perhaps you have even given out a negative rating. And what happened then? I would bet that in 99 out of 100 cases nothing happened. At least you didn’t notice anything of a consequence, a thematic discussion, or an improvement. So: nothing happened for you. Nobody came forward. Nobody asked questions, apologised or praised improvement. And what did that do to you? Were you enthusiastic? Were you happy? Did you report this “great” experience to your acquaintances or colleagues?

What do you think of the idea of making concrete use of existing information and no longer ignoring it? Sounds smart, doesn’t it? 😉

Small Data for your employees

What applies to customers also applies to colleagues, employees and superiors. Here, too, there is a wealth of information that is easy to use. Time recording, for example, is not a control instrument for nothing and at the same time serves as self-protection. What do you do with employees who work a lot of overtime? How do you deal with colleagues who regularly suggest improvements for company-wide idea management? How do you evaluate the training wishes of individual employees? How modest are your requests for home work and the implicit desire for more self-determined work? What do you do with an employee who wants to bring his dog into the office? Or what do you do if an employee wants a salary increase in order to receive the same payment as his colleague?

With regard to employees, there is an almost infinite amount of data that you can easily analyse, evaluate and use. By this I don’t even mean the information that is expressed verbally or non-verbally in daily interaction and is not stored on a machine. I’m referring to the numerous pieces of information that end up or have already landed in the relevant systems. Which can be read. Which are available as a data set with a 1:1 relation. Which are really “small”.

The good news

Often, organisations store information from and about customers and/or employees. In CRM systems, employee databases, Excel lists and other programs. In some business areas this data is used regularly, e.g. for the planning of new features or the development of products. Far away from such development areas, however, available information is rarely used purposefully. For me this is surprising, because it does not require a Big Data approach for the analysis. There is no need to link 20 different data sets stored in 5 different systems. It does not require a combination of data after an analysis of one million pieces of information, nor a mathematics degree. All it needs is a clear view, and the willingness to improve at a certain point. Surely you can offer a faster service with your organisation. And actively use feedback for customer care. And promote employees. And improve the working atmosphere. And …

The good news is: you already have everything you need for Small Data in your company. All you have to do is use it. Of course ideally smart.

Hints:

For the sake of better readability alone, I have refrained from using male and female language forms at the same time. All personal designations apply to all genders.

[1] Big Data at Wikipedia (in German)
[2] Small Data bei Wikipedia
[3] Smart Data bei Wikipedia (in German)

Michael Schenkel
Michael Schenkel

Head of Marketing, t2informatik GmbH

Michael Schenkel is a graduate business economist and is passionate about marketing. He has a certificate for excellent hiking characteristics, Odenwaldtour in classes 6a/6b and since 1984 the Seahorse. He likes to blog about requirements engineering, project management, stakeholders and marketing. And he will certainly be delighted if you meet him in the real world for a cup of coffee and a piece of cake or for a virtual get-together.

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