All IT vendors propose to help businesses transform their organizations into digital machines, become data-driven enterprises capable of leveraging information to better serve customers, improve worker productivity, adapt or reinvent products and services in order to find new routes to market or revenue streams.

However, each company’s path through digital transformation is unique and just this week two articles appeared that brought this reality to light in a rather mind-blowing fashion.  The articles presented two separate case studies on digital transformation whose results were diametrically opposite.

In the first study—the 2017 study BPI France Le Lab, which interviewed 1,800 executives of SMEs – found that 87% of Small and Medium-sized enterprise (SME) leaders have not made digital transformation a strategic priority for their company.

The study also reported that 61% did not utilize sales or customer data for decision making. I would be willing to bet that if they do not exploit their sales and marketing data, they don’t exploit other types of data either.

In the second study—the ACSEL and Ipsos Growth and Digital study, which interviewed 606 SME companies—said that 71% of companies saw digital as a driver of growth. Further, 85% of respondents said they have begun their digital transition and companies that have undergone digital transformation are 2.5 times more likely to have stable growth.

So what’s really going on here?

Most reports today say that companies that are successful in their digital transformation objectives take a top-down approach. Meaning the CEO is at the forefront of digital transformation, building a vision, setting the course and sharing it with his teams. But the BPI France Le Lab study shows that only 25% of managers associate their teams with the project and 12% train their digital collaborators.

While the above is certainly true, what if companies adopted both a “top-down” transformation model, in parallel with a bottom-up approach? What if it were possible to enable more employees to discover the data that exists and then manipulate, analyze and take advantage of that data to improve the business?

For several decades, companies have tried to make their employees “data-driven” by creating Business Intelligence Competency Centers. But despite huge investments in data management, business intelligence, and data visualization tools, studies show that only 10% of employees are actually “data-driven”, and these are mainly data analysts.

This model may have worked in the past, but it doesn’t meet the changing demands of today’s digital world, such as the explosion of data volume, variety, and complexity, compounded by the increasing number of users demanding access to data. According to Gartner, “Improving employee access to data and information (via better and new search services, proactive information delivery and data discovery tools) is part of the digital workplace initiative to democratize analytic capabilities. New information access mechanisms (such as social and office graph services, along with virtual personal assistants) will supply consumer-like information retrieval and alerting services. Finally, engaged workers are more likely to share information in an open forum, further increasing the supply of data.” (Gartner, “What to Do When) Every Employee Is a Digital Employee,” September 2016.)

Your employees become the engines of your digital transformation

Faced with these new data challenges, there is no other choice than to pave a way for self-service data in each and every business. Employees should be given access to not only company data, but also all external data sources such as partners, suppliers, and open or government data as well.

By giving LOB employees power over the data they know best, their creativity can be unleashed: new micro-market segments identified, consumer needs better identified, better mapped and better anticipated, better identified sources of profitability, new products and services invented and better marketed, product life cycles better understood and better predicted, etc.

Thus, the culture change needed for digital transformation to really take hold can happen, and employees can become the engine of transformation.

How to open self-service data to liberate autonomy without taking risks?

Delivering this self-service model through a cloud application accelerates adoption by breaking down all the traditional barriers: no install of applications is required with time wasted and the difficulty of having sufficient privileges to install software on its PC, no upgrade required to be able to benefit from the latest technologies and features, no maintenance.

Delivering this cloud self-service to simplify access to all data sources (including cloud applications, cloud databases, IoT sensors, data lakes, open data, etc.) and integration with all applications and destination systems (SaaS or on-prem applications, data lakes, databases, etc.) allows to extend the capacities of each user, to some extent to increase his intelligence.

Adding a wide range of intuitive and semi-automated data preparation functions to this cloud-based data access and data integration enables anyone – regardless of their data expertise – to transform, clean, format, standardize, enrich the data according to the business logic that he best understands. In 1 word, improve data quality, while 84% of managers are concerned about the quality of the data on which they base their decisions according to the Global CEO Outlook of KPMG in 2016.

Finally, delivering this cloud self-service for access, preparation and data integration on a secure IT platform and managed by the vendor on the cloud ensures that users will not access and will only work on the data to which they are entitled. This model also ensures that users define the access, data preparation and integration scenario once and then automate and deploy it across the enterprise.

Our latest release, Talend Fall ’17 (start your 30-day free trial now) integrates Talend Data Preparation Cloud supported by Talend’s Integration Platform-as-a-Service (iPaaS). This offers an on-demand cloud application to access, prepare and integrate all your data:

  • Whether you are a business user, analyst or data scientist, you can intuitively and quickly access the data you need. Whatever the cloud or the system it lives on.
  • You always leverage the state-of-the-art technologies to clean, format or combine them.
  • You innovate with data, IT provides you with service and secures it, Talend manages it in the cloud.

You contribute to your compliance with GDPR

With Talend Fall ’17 anyone can be data-driven and it can act as an accelerator of digital transformation projects. By acting in a “bottom-up” mode from the field, Talend Fall ’17 helps to guide companies towards a systematic, governed and reasoned use of data. Isn’t that one of the main objectives of GDPR?

GDPR exists only because data has exploded for every company. Effective May 25, 2018, the GDPR laws aim to protect the privacy of individuals by governing the management of their personal data. And it gives arms to the regulator to act against the offenders.

For any company addressing citizens of the European Community, these are:

  • Identify, know and track personal identifiable information (PII)
  • Ensure that they are compliant
  • Protect and foster accountability
  • Make them available especially for their anonymization, the right to forget, etc.

Talend Fall ’17 can help you discover and capture PII in your datasets, only share them with authorized users thanks to data masking and data shuffling functions.

For Talend Integration Cloud users, Fall ’17 is now available and two free Talend Data Preparation Cloud user licenses are automatically included in any subscription to Talend Integration Cloud.

If you are not using Talend Integration Cloud yet, start your 30-Day free trial now by identifying a simple use case such as automating the enrichment of your CRM or automating the analysis of your marketing campaigns.

Find out: Talend named a Leader in ‘Data Quality Tools’ Magic Quadrant

 Why Data Quality Should be the ‘Red Thread’ of your Data Strategy