The advent of the internet, social networks, real-time analytics tools and artificial intelligence are profoundly changing almost every business process towards being data-driven, fostering almost ‘scientific’ approaches to business management. Indeed, this creates a considerable reliance not just on the data itself, but on the underlying data infrastructure as well. Since non-technical departments such as marketing or sales often tend to be primary drivers of this shift, the underlying infrastructure has to reflect the fact that it’s operators typically do not have a degree in computer science.
‘Shadow IT’ fostering siloed analytics within organizations
Arguably the most crucial factor of changes in the modern business environment is the availability of real-time (or near real-time) data to make qualified business decisions. 20 years ago, it was pretty common to conduct research in the form of financial analyses, focus groups, questionnaires or marketing studies prior to each critical decision. The issue with these practices is that they were often rather occasional and performed on a small scale, therefore past experiences of business leaders and their ‘gut feelings’ were essential ingredients of the decision-making process. With the advent of digital products, online customer-facing systems and digital sales channels, there is a vast surplus of various analytics tools that can provide unprecedented insights. However, the adoption of these tools and services (mostly delivered as SaaS) was usually not driven by the overall IT strategy, but rather end-users creating what some call the phenomenon of shadow IT. Since many services such as Salesforce, Zendesk or Google Analytics gradually grew into enterprise-grade softwares, organizations usually built siloed teams and capabilities on top these systems.
Data integration platform as the centerpiece of data fabric
Adoption of data analytics within organizations naturally forces the centralization of analytics capabilities since the previously mentioned siloed architecture is rather expensive and creates frictions between internal teams and departments. In support of this claim, renowned IT analytics and research firm Gartner identified data fabric as one of the critical data and analytics trends. According to Gartner, “Data fabric enables frictionless access and sharing of data in a distributed data environment. It enables a single and consistent data management framework, which allows seamless data access and processing by design across otherwise siloed storage.” Since data experts find it extremely painful to work with analytics data from multiple web, social, sales or customer platforms, we developed the Dataddo integration platform that helps our customers to centralize all their analytics data into a single point; whether it is a dashboarding app like Tableau or Google Data Studio, their data-warehouse or cloud-based big data storage.
No-code platforms as an answer to increasing demand for technical skills in non-technical departments
The trend of proliferation of data analytics in the organisations result in another outcome; increasing requirements on non-technical employees (usually in marketing, product or sales) to deal with tasks previously dominated by people with a technical skill set such as data-prep scripting, SQL or data architecture. Indeed, it is not uncommon that basic knowledge of coding and working knowledge of technology is often considered as one of must-have skills for marketers. However, marketers are not coders and vice versa, therefore it is important to provide the non-technical staff with tools and platform, allowing them to perform complex operations, but still having ‘human’ interface. Therefore the no-code / low-code platform is the answer for solving this issue.