Within the Futures By Design project, we work together on different tools and formats which we use to support organisations. The purpose is to help participating SME’s with their next step to become more data driven. The developed tools help to outline the current situation, define the problem statement and explore the possibilities.
Every tool has its own added value and is helpful in a different stage of becoming more data-driven. Before starting a project, it is useful to know the data maturity level of your organisation. Below is the recommended order in which the tools can best be used and create the most value *.
Currently we are still busy developing new tools. Keep an eye on our website!
* Some SMEs may not require all steps or a different order. This is in consultation with the partner.
Data Jumpstart + Data Report
This scan consists of a set of 40 questions that dive deeper into various aspects of data maturity. For example, we look at the infrastructure, tools and culture within the organization. Every company that starts with the FbD process completes this scan. When the Data Jumpstart has been completed, we get started with the outcome. You will receive a report in which we break down the results and benchmark them against the reference group.
To support you in the best possible way on your journey to become a more data-driven company, it is important for us to get to know you and your company a little better. Therefore, we created this assignment with several questions about your company. What do you encounter in your company and where do you see opportunities with data? What do your employees and customers think of your ideas?
Preparing for the FbD Project
Data inspiration booklet
In order to give the SMEs that are participating in the FbD process a better picture of what is already possible for SMEs in the field of data science, we have created a booklet in which some examples of projects within SMEs are illustrated.
The Data Jumpstart tool provides a level of data maturity for your company. The examples in this inspiration folder are also divided into these 5 data maturity levels, hence you can easily see which example projects are feasible for your data maturity level.
Most entrepreneurs who want to start the transition to a more data-driven company run into the following question: “Where do I start?”
This guide has been created with the aim of helping you determine your starting point. You decide on which part of your company you want to focus, you gain insight into your main motivation to get started, you define your ambition and challenges and ultimately work towards the challenge that requires the least effort and represents the most added value for your company.
“How to determine focus” guide
Data structure guide
Before we move on to making cool predictions, it’s important to know if the data is suited for this analysis. The data structure manual explains how a company can best check whether the data is collected correctly. It is important here that the data is consitent an accurate. For example, you can write down a telephone number in several ways 0612345678, +31612345678, 06-12345678. In all cases the same is meant, but notated differently. For further analysis it is important that the data is clean and structured.
Many SMEs are not yet familiar with the quality of their data. The Data Jumpstart shows where the organization stands in the terms of data maturity. Part of the scan is checking the data quality. For example, there may be a lot of empty values in certain columns, or a negative number for an invoiced amount. The Data Exploration tool has been developed to determine, as an organization, where the data quality can be improved. The data can be uploaded in a simple manner and the company will receive a report containing the various findings of the data.
Do you want to know how fast your website is? Or what similar websites are? With the Footprint tool you immediately get an overview of your website. This contains information about your social media accounts, contact information, most important keywords of your website, a short summary of the content, comparable websites and the loading speed. This allows you, for example, to compare your website with competitor websites.
A data sources checklist has been developed to check the quality of your data sources. With this, the available data sources are mapped, but also described which are relevant within the organization by using the 4 Vs of Big Data. For each data source, questions are asked such as “Is it an open data source?”, “Is it sensitive data from a privacy perspective?”, “How was this data collected?”. By providing the answers to these questions, you can think in advance whether you will run into problems with a data science project.
The Data Brainwave is a tool which helps a company identify specific and feasible projects in the field of Data Science. By filling in the twelve boxes in the canvas, it becomes clear where the opportunities and challenges lie with regard to working with data. The filled canvas provides a Data Scientist with the necessary information to eventually define a project.
The Data Brainwave distinguishes between three main categories:
- Knowledge infrastructure
The extent to which various software is currently used, the expertise in-house, or collaborations with IT parties.
Prior to a project, consideration must be given to the commitment from different (management)layers, the available budget and the application of regulations.
- Expectation management
By considering the expected results and ongoing challenges in advance, the chances of the project succeeding are greater.
The twelve sub-topics on the canvas have been determined based on more than fifty Data Science projects and scientific research. The research shows that it is difficult for companies to start with Data Science. This Data Brainwave can therefore also be used as a stand-alone tool. However, some partners also offer the option to complete this canvas together. Ask the affiliate partner for more information.
The Data Booster has been developed to convert the results of the Data Brainwave and Data Jumpstart into actions. A brainstorm session is organized with the affiliate partner. The insights from the various used tools are discussed and used to start with a first specified step. During this session, we look at what else the company needs in terms of tools or support to achieve the formulated goal. After this session, the company or another commercial party can immediately start a project. In some cases, the partner can also support in the implementation of the project.
The Data Project Ethics Assessment (DPEA) is intended as a decision-making tool to help data (science) students, practitioners and entrepreneurs start a data science project. The DPEA consists of a series of questions covering some, but certainly not all, important ethical considerations. Filling in the form gives a global picture of the ethical impact of the project. This could then affect the choice to start the project, make changes, or stop it altogether and abort it.
Data Ethics tool
Whether you are working with the data within your own company or working together with another party, it is very important to also consider how you handle your data safely. We have made a small checklist for you with several tips on how to handle data in the safest possible way and what you should consider when thinking about working safely with data.