As markets become increasingly complex and competitiveness rises, the capability to flexibly plan, analyze, and…
“Self-Service Analytics,” according to Gartner, “is a form of Business Intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.” Put another way, Self-Service Analytics is a way to empower employees to make better decisions through alignment to organizational goals and objectives.
In the Office of Finance, the impetus is on the CFO and their team to enhance the business with key insight and drive decision-making processes that transcend the finance department. Data is essential to this demand, providing the foundation for analysis and planning. To this end, Self-Service Analytics increases the availability of data for staff to better analyze trade-offs between the speed, cost, and quality of various business decisions.
A robust Self-Service Analytics solution is now a necessity for businesses and the finance function. When adopted correctly, a Self-Service Analytics platform will:
Self-Service Analytics empowers employees to make better decisions but achieving the above benefits goes beyond just having a Self-Service Analytics tool in place. To get the most out of both the tool and the concept, consider these four tips.
A common theme in modern business planning and operations, spreadsheets have, for a long time, been the preferred way of working. Companies often struggle to put this legacy way of working behind them. As a result, high levels of spreadsheets are introduced and maintained, inefficiently tracking data and creating a reporting ecosystem that gets exponentially harder to maintain.
Consider this for a typical finance team in a large organization: each team member has their spreadsheet (to manage their tasks). New spreadsheets are added to this pool to monitor or address additional data tools and analysis requirements. As this repository of disparate spreadsheets grows, the effort needed to maintain them grows too. Likewise, the complexity to keep everything aligned alongside the heightened risk of errors (from manual data entry and incorrect formula equations) all adds together to become a chaotic and laborious responsibility.
So, what happens when businesses break up with spreadsheets and adopt powerful digital platforms that combine data across the organization? Suddenly teams have an integrated approach with significant benefits:
As a result, the capability to plan, analyze, and forecast is enhanced. Additionally, these benefits align with Self-Service Analytics by making it easier for all users to access standardized data, understand it, analyze it, and drive decision-making processes from finance and across the business.
Adopting new decision-making platforms, and consequently, Self-Service Analytics tools, will raise the question of balance between business operations and IT. In a broad sense, IT is the data owner for the company. While this does not mean they are responsible for every aspect of data management, it means they must ensure the integrity and security of all company data. How it is stored and secured is of paramount importance. At the same time, operational departments will manage and utilize data day-to-day, so how they access and move data is of equal importance.
Striking the right balance between business users and IT has its challenges. Ideally, strong executive sponsorship will guide this alignment. IT, department leaders, and finance must work together, and the team must be driven by someone who will achieve the right balance. Consistent business rules and terminology must be captured so that analyses generated by two different departments on the same metric return the same results.
Overall, a balance is required to overcome the broader complexities of integrating new digital solutions into the organization while reaching the disruptive business value of decision-making.
Not to be confused with Artificial Intelligence (AI), though it does find a grounding in AI, cognitive technologies focus on how technology can interact with people by mimicking functions of the human brain. Put another way, cognitive technology is about creating more human-like interactions with technology so users can interact with them easier. Natural language processing, data mining, and pattern recognition are all elements that come under the cognitive technology umbrella.
With cognitive technologies, businesses can revolutionize their digital architecture – including Self-Service Analytics – by having tech that is easier to use, easier to comprehend, and easier to adopt from a user standpoint.
For example, users can ask questions in their natural language to launch in-depth search-driven analytics. This interaction has far-reaching potential to onboard users with what could be complex systems and functions. It also has the potential to significantly increase the speed of decision-making processes, and is already delivering this in companies currently utilizing cognitive technologies.
While the benefits of cognitive technologies can be highly beneficial, the foundation for integrating them must be robust. This means, for example, that data must be reliable. As already discussed in Tip #1, the inherent issues with spreadsheet proliferation highlight that data value is questionable. If data is disparate, at risk of incorrect entry, and poorly managed, the basis for the cognitive technologies is built on unreliable data as a result.
A unified approach to data management must be in place to truly enhance business users’ self-service capabilities with cognitive technology. Knowing how the data has been collected, normalized, and managed helps ensure its reliability. And having a single source of truth ensures that everyone is working from that same trusted data source. In short, a robust Self-Service Analytics platform based on a single dataset is critical to this tip’s success.
When utilizing an integrated and unified approach, the benefits of Self-Service Analytics are greatly enhanced. The insights gained are beneficial but only when the user and business can leverage them properly.
The value of standalone Self-Service Analytics tools is inferior to a standardized approach delivered in one platform. Look at it this way, if Self-Service Analytics is not seamlessly integrated into the decision-making process, then analysis and insight will not hit the next level and maximize its impact. While analytical insight can be leveraged in the first instance, it does not have the speed and adaptability to enhance the quality of decision-making processes.
This standardization – also known as Integrated Business Planning (IBP) and analytics – is considered a best-practice approach where financial, strategic, and operational data is incorporated from across the organization. It enables companies to maximize their output by linking strategic plans with sales, operations, and finance to create greater visibility of the relationships that link resources, processes, and results.
In turn, this delivers one set of reliable data, a single version of the truth, which cannot be contradicted elsewhere. With this, the organization can reach an alignment that spans company-wide strategic initiatives to individual goals, pushing the entire business to move in the same direction.
The overall value of this should not be underestimated. Business goal alignment led by trusted data will prove a key driver in revolutionizing decision-making through a digital transformation initiative.
The theme that runs throughout this article is the idea of integration and standardization. Having all financial, strategic, and operational data in one platform that delivers Self-Service Analytics and BI, and IBP and decision-making insight is paramount.
Through this, embedded planning and predictive analysis are possible. The rapidity of decision-making workflows adds undeniable robustness to react to a competitive market – no matter the industry.
It’s worth looking beyond a pure Self-Service Analytics tool to consider a Decision-Making Platform that delivers these capabilities alongside embedded planning and predictive analytics. Not only will this save time in the long run, but it will also enhance performance business-wide.
The future resides in a platform that ensures data quality and integrity for Self-Service Analytics and provides a highly scalable modeling environment that leverages the same user-friendly concepts. For this reason, a Decision-Making Platform is best suited to meeting modern Office of Finance needs, providing the visibility and capability to manage all Self-Service Analytics requirements while linking FP&A with strategic and operational activities, too.
The themes and tips explored in this article cover important considerations when it comes to adopting modern Self-Service Analytics and how it can weave into the FP&A process. Download this eBook to expand on the subject and learn more about:
Self-Service Analytics, boosted by cognitive technologies, can provide business leaders with the information they need for better decision-making through more readily available insights. But what should you consider when it comes to modern Self-Service Analytics?