For many organisations, data is at the centre of fundamental decision-making, providing business leaders with guidance on how and when to act. But this new emphasis on analytics may leave technical teams burdened by a backlog of additional tasks while their non-technical counterparts struggle to contribute.
As companies become more connected and data-driven, the question is no longer if data should be made available to more people in an organization – even everyone in an organization - but how.
Of the estimated 130 software offerings in the analytics and business intelligence (BI) space, few have evolved to meet the needs of modern, data-driven businesses. Where most BI products have yet to catch up, technical professionals are filling in the gaps to support line-of-business staff. A growing number of enterprises are working towards company-wide adoption to turn data into actionable insights across the board, starting with the embrace of augmented analytics tools that incorporate artificial intelligence (AI) and machine learning (ML).
Calls for continuity
The use of different BI and analytics tools by different teams and departments has created a complex IT landscape that often obscures trends and insights instead of revealing them: silos, shortcuts and inconsistent KPI reporting abound. This lack of continuity makes it difficult for business leaders to accurately anticipate trends or disruptions, and the steps they need to take to course correct or capitalise on opportunities.
“You have a single CRM solution for everybody in the company to manage customer relationships,” says Omri Kohl, co-founder and CEO of Pyramid Analytics. “You have a single ERP to manage your financial - and potentially your production - environment. But you sometimes have hundreds of localised ad-hoc analytics initiatives, so you end up with lots of siloed implementations that advance a specific function but don’t provide a view of the entire company.”
These siloed implementations have occurred against a backdrop of explosive data growth. A decade ago, businesses had limited access to data and accurate, accessible insights that analytics provides. Today, data is abundant, whether structured or unstructured, in small, independent data sets or large inflows of insights that talk to one another. The value is in how information powers decision-making across the business.
This vast wealth of data is constantly in motion. “Data used to be stuck in an enterprise data warehouse,” says Kohl. “Now it’s moving to data lakes. It’s moving from on-prem to the cloud. It’s moving from cloud to multi-cloud, and so forth.” Keeping up with this sustained movement is a challenge and business intelligence needs to adapt to support new levels of operational flexibility.
Democratising decision-making
Navigating this complex data environment and using insights to drive smarter business decisions requires clear leadership. “If I’m a manager of a group of people and I don’t make data-driven decisions, my team won’t make data-driven decisions,” says Kohl. But all too often, managers who want to apply data to make smarter decisions are faced with a debilitating lack of technical know-how.
Department heads want to analyse data in a way that enables them to detangle functional insights from assumptions and use these insights to report with confidence to the executive leadership team. To achieve this independently, they need access to nuggets of information that can inform timely, intelligent decisions.
However, many analytics and BI tools are still targeted at the needs of technical users rather than the needs of the business itself. They require extensive training and skill to use, which makes them inaccessible to the everyday line-of-business user. If a manager wants to understand why a certain area of the business is performing in a certain way, they need to call in the BI experts. And the result is slow – or even no – decision-making.
The Pyramid Analytics Decision Intelligence Platform is a prime example of a streamlined, unified and personalised decision intelligence platform that allows non-technical employees to access and analyse multiple data sources in an AI-driven no-code environment. “Everything is drag and drop and point and click,” Kohl explains. “You don’t need to know how to write code… [but] you can still do very deep, very sophisticated research on your data.” Increasing the availability of analytics and creating greater overall visibility means anyone can draw valuable insights from data flowing into the business quickly, without relying on hypothetical assumptions.
Going one step further, prescriptive analytics uses AI to offer actionable recommendations based on insights and trends, as opposed to speculation. The outcomes can be tangibly financial, organisational, and even environmental. A power plant, for example, could determine what mix of energy sources they should be using to maximise efficiency and minimise their carbon emissions, without bringing in analytics experts.
Personalised experiences
Data is integral to almost every business function, so finding a more accessible way of interacting with analytics is key to developing a healthy data culture across teams. The onus is on business intelligence and analytics tools to meet organisations where they are.
Non-technical users who are uncomfortable using even basic point-and-click and drag-and-drop BI tools can still intuitively interact with data visualisations using plain English, via a Natural Language Query (NLQ) chatbot. Pyramid’s NLQ chatbot, for instance, allows users to type or use voice commands to query data natively and directly in the data source, regardless of location, size or complexity. This means anyone in the business can fully investigate data without needing to know the underlying data structures, hierarchies and measures, with the platform generating actionable insights that can then be shared in a report or presentation.
“If you see an underperforming salesperson…you can start asking ‘is it the product that he’s selling? Is it his territory? Is it the demand generation engine behind him? Is the cost of the solution that he’s selling too expensive based on the competition?’ You can start peeling the onion, even as a non-technical user, and go very deep,” Kohl explains.
AI and ML can tailor the analytics environment to an individual, ultimately generating more data-driven decision-making. “It could be automated insights that we push to your morning dashboard: ‘here are the five things you need to know about your business that happened in the last week’,” Kohl explains.
Democratisation is a win-win. Reducing one-off requests means freeing up data analysts to focus on more strategic work. Instead of providing reports and dashboards, they are empowered to provide predictive and prescriptive insights that allow them to identify core business strengths and double down in those areas.
For this to work, companies need to develop a lateral approach to analytics, equipped with intelligent tools that bring new voices into the conversation.
Q&A: Pioneering inclusive artificial intelligence
Gauthier Vasseur is executive director of the Fisher Center for Business Analytics at the University of California, Berkeley, Haas School of Business and co-president of the Alliance for Inclusive Artificial Intelligence (AIAI).
AIAI’s core mission is to inspire and empower women, underrepresented minorities, and people from lower socio-economic backgrounds to successfully pursue an education and career in analytics. The Pyramid Decision Intelligence Platform supports Vasseur’s ‘Step Into Data’ workshops, which have provided more than 2,000 learners in 30 countries with hands-on experience in data analytics.
What has traditionally limited access to AI, analytics and data-driven insights?
It’s easy to use our gut feelings, to keep going with our own biases. But as soon as you become data-driven, your biases are exposed and that’s not something we naturally want. It forces us to change; it forces us to be humble, and that’s not always easy.
Secondly, good, qualitative data – data that is prepared, curated and ready for analysis – is much scarcer than you might think. A lot of people say, ‘I’d love to have more insight’, but then they get their hands on some data and it’s a mess. You’re not going to get much from data that is just raw, that is not indexed, or that is not linked.
Thirdly, there is still a belief that data analytics means data science and coding, so it is ‘not for me’. What’s changing these days is that we have low-code, no-code approaches. This empowers you to do the same thing you would do with coding much more easily and to some extent, much faster. But even better, it makes [data analytics] more accessible.
Why was the AIAI established and what do you hope it will achieve?
Our mission is not only to promote research and awareness; it’s also to promote the application and the impact of business analytics. That application and impact only happen if anyone can do it.
We realised that in today’s world, unfortunately, women and underserved communities don’t have access [to business analytics]. We will never have good quality, sustainable, humane, ethical analytics if diversity of profiles aren’t included and if it fails to represent the whole world. So, the AIAI was born from that.
Companies, administrations, and governments need to train their people. So, our proposal is: train your people with us and for each person who trains, we will offer one free Berkeley Extension business analytics certificate for a woman or [member of an] underserved community. This leads to something quite self-fulfilling. As you struggle to recruit the right people, guess what? We have certified women and [people from] underserved communities who will follow the same workshops as your employees and will know exactly how to work with you.
What has your partnership with Pyramid Analytics brought to the AIAI?
The partnership with Pyramid has been fantastic from the get-go. Even when we were just starting out there was full trust: we understand what you do, and we are going to help you. That is not common.
Communities that have been left behind need to catch up, they need to be operational, and they need to find a job. So, all our classes are designed to be hands-on: I learn, I do, I learn, I do. And that’s why we work with Pyramid Analytics, because it’s fantastic for learning everything about a data process and then applying it right away in a no-code environment.
It’s a tool which allows any student, anywhere in the world, on any machine, to run world-class analytics without having to go through a tedious install process, [access] powerful machines, you name it. The feedback we get is that they can’t believe they can do all this. They feel empowered, and that’s a game changer.
How can greater access to data analytics bring about positive change in the world?
Your mindset radically changes once you dare to acknowledge a problem because you’re empowered to ask the right questions. You can’t do magic. But if there is a solution and you have the data, you feel empowered to find it. That’s the level of confidence we give to women, underserved communities, and all our professional trainees.
To learn more about applying augmented analytics, decision intelligence and AI in your enterprise, visit pyramidanalytics.com/decision-intelligence-platform
Promoted by Pyramid Analytics
For many organisations, data is at the centre of fundamental decision-making, providing business leaders with guidance on how and when to act. But this new emphasis on analytics may leave technical teams burdened by a backlog of additional tasks while their non-technical counterparts struggle to contribute.
As companies become more connected and data-driven, the question is no longer if data should be made available to more people in an organization – even everyone in an organization - but how.
Of the estimated 130 software offerings in the analytics and business intelligence (BI) space, few have evolved to meet the needs of modern, data-driven businesses. Where most BI products have yet to catch up, technical professionals are filling in the gaps to support line-of-business staff. A growing number of enterprises are working towards company-wide adoption to turn data into actionable insights across the board, starting with the embrace of augmented analytics tools that incorporate artificial intelligence (AI) and machine learning (ML).
