What are the biggest challenges for companies looking to digitize their core functions and processes?
With around 2.5 quintillion bytes of data created each day, one of the biggest challenges for contemporary organizations to address is how they can refine the data available to create the data-driven insights needed to stay competitive and drive the business forward. While the past year has accelerated the pace of transformation initiatives to a new level, it has also demonstrated that data is the lifeblood of sound, responsive decision-making. In good times or bad, organizations need answers, but to become data-driven, answers now need to be provided at a speed once thought unimaginable.
The only way to surface these mission-critical key insights daily is to provide everyone with the ability to harness the superabundant amount of data available. This means making data more accessible than ever by providing intuitive and engaging technology to the people in the front lines of the business, so they have the ability to analyse it and answer questions at the speed of business. Understandably, this has compelled many companies to invest in IT and data-driven, hungry technologies, such as process automation, artificial intelligence, and machine learning to try and leverage their data for competitive advantage, but humans also play a critical role.
However, dropping new technology into a broken business process won’t boost productivity or efficiency. organizations looking to digitise their core functions and processes need to think carefully about how they can best combine technology, people and processes to build a culture that instils a set of behaviours internally to really uncover the challenges and, as a result improve current processes.
Unfortunately, with most organizations there is often a lack of alignment on the technology focus, and we therefore can see all three of these components existing in disconnected silos. This is when digital transformation can fail, and this is what stands in the way of delivering successful accelerated outcomes. If you have access to the right technology, but with bad data, you’re only exacerbating the data challenge. If you have limited technology, only a small subset can do anything with the data.
By leveraging easy-to-use modern technology built specifically for data science and automation, organizations can better meet today’s challenges. Those businesses that use data analytics at the start of their transformation journey gain a competitive advantage as the changes are noticeable and have impact.
Which key analytics breakthroughs define transformation in 2021?
High-value business outcomes start with high data quality. But with the scale and complexity of modern data, the only way to truly harness its value is to automate the process of data discovery, preparation and blending of disparate data. Automation is essential because, first of all, it frees up the analyst to focus on the high value-add activities, which really drive top-line growth. Secondly, it helps contribute to the bottom line by trading out mundane activities for more efficient processes.
Industries such as manufacturing, travel and hospitality, retail and financial services are all benefiting from this trend. The retail industry, for example, has undergone multiple pivots in recent years and is leveraging machine learning and data science to gain an edge. But experimenting with different machine learning models and waiting for them to yield results takes time. To get answers at hyperspeed, you need analytics automation.
Getting ahead of your competition today means rear-view reporting doesn’t cut it. By using a contemporary analytics automation platform that makes reaching insights easier and automating daily merchandising tasks, a leading retailer grew its top line by nearly $1.5 billion. Automation is the “superpower” that enables data-driven decision-making.
But what’s paramount is that everyone in the workforce should be able to do it, not just a handful of specialists. In the past, employees with vast expertise in their own fields were locked out of data analytics due to the niche knowledge it required. They had to rely on tech and analytics experts to convert noise into signal. Platforms and technologies that can only be leveraged by data scientists won’t deliver the best outcome, as there are simply not enough data scientists to answer all your business questions.
Now, thanks to self-service platforms and automation, the power of analytics is no longer restricted to a few gatekeepers, but rather it is available to all. Accelerating the knowledge workers’ journeys to become data-driven.
Which teams are experiencing the benefits of adopting automation and analytics?
Accounting, tax, and finance have some of the most data-intensive processes within any company. Revenue, expenses, forecasting, and reporting require numbers from every part of the organization. The volume of data arriving from many of these sources often makes getting everything together difficult. Making sure those numbers are accurate for building reports to communicate them can be even more complicated when using yesterday’s tools for today’s data sets.
Data science and automated analytics is changing that and revolutionizing finance departments from the ground up, replacing common routine manual finance tasks such as payroll and tax calculations with end-to-end automated processes.
Finance teams frequently need work with data from across specialized systems (Concur, ADP, etc.) in order to complete more complex tasks. These can often be replaced through data automation. Providing more time for data workers in the finance departments to forecast and develop financial strategies that were previously ‘heavy manual and repetitive lifts’ for the analysts in these departments. What might have taken a month to forecast previously can now be run three/four times a day, so that the C-suite can make better decisions.
What’s the biggest benefit of automation technology for customers in financial services specifically?
The speed at which you can produce results and solve problems. Automation fundamentally allows financial services teams to transition from lower-value spreadsheet work, controls and reporting, to higher-value decision support, insourcing and strategy work. For example, end-to-end analytics automation can enable tax teams to streamline and accelerate traditional tax work, provide more analysis and guidance across business lines and help preserve their companies’ profitability. Tax departments who automate processes achieve results like lower administrative costs by reducing time-wasting manual work, less tax risk by identifying accounting errors in near real-time and optimized tax through better scenario planning and analysis.
Early adopters are discovering the benefits of tax automation with a single-use case that focuses on implementing specific technology within their company’s environment. This then creates the foundation for digital scalability and momentum through additional areas of finance.
Are there any country-specific differences that can be identified?
The UK is definitely one of the front runners for the use of advanced analytics and data, however, there are also some interesting parallels that can be drawn and comparisons that can be made with other countries. In Germany for example, it’s been obvious in the context of the pandemic that many insurance companies weren’t prepared and had to adapt extremely quickly in order to simply continue doing business in the face of the situation.
In the Netherlands, the landscape is typically very opportunistic. They tend to start with a proof of concept and then take it from there. It’s more proactive. This is in stark contrast to Germany, where it’s more about having a structured approach in place before diving into anything new. Interestingly, France has a huge banking sector, which includes the likes of Société Générale and BNP Paribas.
The focus there is also very much on digital, which they believe is the way to go. The French start-up scene is booming, with a lot of landmark developments taking place there on a daily basis.
Suresh Vittal, Chief Product Officer, Alteryx