How Analytics Platforms are Enabling an Insights-Driven Business Model

How Analytics Platforms are Enabling an Insights-Driven Business Model

Having completed my first Quarter at Yellowfin, I’ve been reflecting on what makes the Analytics & Data Visualization sector such an exciting space in which to work. What are the disruptors and trends?  What are the changing requirements of our clients, and how has Yellowfin been improving our products and services to meet them?

 

Towards an Insights-Driven Business Model

 

The most interesting aspect of Analytics right now is the rapid pace of innovation as we seek to support clients’ journeys from a data-driven, to an insights-driven, business model. Insights-driven means understanding not just what business outcomes have occurred, but why. It refers to companies that use data to take strategic actions and create a competitive advantage. It relies on data to drive agile cycles of learning and innovation and keep teams accountable for business outcomes. Forrester research shows that insights-driven businesses are growing 8-10 times faster than those that are simply data-driven, and are on track to earn £1.8 trillion by 2021.

 

Revenue Growth & Customer Satisfaction

 

Helping small to medium enterprises achieve an insights-driven model means making data insights directly accessible and comprehensible, at the push of a button, even to those key decision-makers who are not from an analytics or data science background. Users like the CDO and CEO are seeking to mature beyond simply using data for process improvement to meet regulations and reduce business risk. They want to use data insights to grow revenue, improve customer experience and satisfaction, facilitate their employees to do their jobs, and show the Board how the company is doing. They want to see a quantifiable, tangible return on investment for their BI platform and infrastructure.

 

BI Disruptors & Trends

 

In today’s market, querying and reporting, data visualization, descriptive analytics, end-user self-service, scalability, administration, and database connectivity are all considered the basic requirements of a conventional BI platform.

To help clients reach a new level of data maturity and become insights-driven, software providers need to harness the power of disruptive technologies like automation and machine learning. These enable features such as actionable and suggestive BI, advanced analytics, enhanced collaboration, natural language narration and metadata development.

 

Data Science & Machine Learning

 

With tremendous computing capacity, we can now analyse larger quantities of data than ever before. It has become more accessible and affordable to use mathematical, data science models to predict and drive business insights. Automation, machine learning, deep learning and plug and play algorithm capabilities allow you to utilise larger data sets and quickly spin up different variations on data science models. There is a mix of tools now available (and often free) to Data Scientists that enable them to do just this, but Analytics and BI teams are still struggling to find ways to embed these models in a way that will result in competitive advantage for their business.

BI Automation

Automation enables self-service data usage and automated data discovery, reducing time to insight; expediting your understanding of context and why events have occurred; and eliminating human bias and error. Many traditional BI and Analytics teams have tried to implement self-service which has had mixed success. In some situations, it enables the technical business user to generate insights themselves but it has also created a maintenance and governance nightmare for IT teams to manage.

Collaborative Analytics

Collaboration reduces the need for independent and repetitive research, speeds up decision-making, and keeps everyone up to date with the current state of each line of business. It also facilitates cross-functional teams and boosts innovation. Ultimately, a more collaborative approach will allow you to break down data silos and build on your own and other peoples actionable insights, for a more agile, iterative and collective way of working. All of these factors have a direct impact on your bottom line and give you a better return on your analytics investment.

 

Yellowfin 7.4

 

At Yellowfin, we now have around 2.2million users across 25k organizations and the most recent version, 7.4, harnesses the power of automation and machine learning to help these clients mature faster to a more insights-driven way of working.

Skilful algorithms and machine learning drive deviation and regression tests to create charts with natural language explanations. Yellowfin’s data visualisations show you not just the fact that there are sales peaks or bottlenecks in your supply chain, but create automated insights to show you why and how they have occurred. You can instantly see and compare year over year, regional, or multiple site results. This means you can decide how to react straight away without the need to manually analyse a spreadsheet or send the data to an analyst to gain further insights. This frees up your data analysts to utilise the more advanced analytics, assisted data discovery and integrated data transformation features, and spend their time adding significant additional business value.

Yellowfin 7.4 offers some amazing features that really start to bridge the gap between the Data Science and BI communities by bringing data-science models, corporate data, and the different business units all together through one platform. Yellowfin does this by pulling data from various sources (relational databases, SaaS connectors or other Yellowfin Marketplace plugins) and orchestrates transformations that bring your statistical models (PMML, PFA, H2O.ai) into business use. For example, you can build flows that run an R model against client data in SalesForce.com or other CRMs to predict churn and deliver insights to business users. With this approach, your R and Python models are made available to all relevant parties via Yellowfin’s data visualization layer and you can leverage its mature services for data governance, security and collaboration. The result is that Yellowfin 7.4 lets you develop advanced analytical applications faster and deliver insights to the business using a robust end-to-end analytics platform.

System of Insights – Next Generation Analytics

In our recent webinar with Forrester Principal Analyst Boris Evelson, he revealed that the average organisation only processes about 50% of structured data and 25% of unstructured data and only 10% and 20% of that, respectively, are turned into proper data insights. If you want to hear his opinions on the how to utilise the latest BI disruptors, trends and best practices to overcome this challenge you can listen to the webinar replay here: System of Insights (SOI): Next Generation Analytics

For me, it’s been a fascinating 3 months at Yellowfin and I’m looking forward to focussing on how automation, machine learning, AI and other emerging technologies will shape the industry in 2018, and what innovations and added value Yellowfin can bring to our customers and partners.

If you want a closer look at Yellowfin 7.4 automated insights, book a demo here.