Start with a “customer-centric” approach to financial analytics

1. Data Collections & Integration: Organization generally have critical business information spread out in various systems viz. Cloud-based data sources, ERP, excel sheets etc. It is important to aggregate all the critical business data into a centralized location via API calls, direct DB connects or other mechanisms. 2. Data Transformation: Data directly coming out of a system is generally in the transactional form which may either need to be aggregated or transformed into a readable piece of information to be utilized by visualization tools. Furthermore, business rules that may need to be applied to raw data also fall under Data Transformation. 3. Data Governance: Designing fault tolerance, incremental refresh, and optimizing other aspects of data engineering are critical parameters to look at while designing a scalable and sustainable BI infrastructure 4. Data Modelling: This refers to the logical inter-relationships and data flow between different data elements. 5. Data Visualization: Finally, all the prior steps feed into a visualization tool to create reports, interactive dashboards for various stakeholders 6. Embedding & Sharing of Report: Mature Organizations have thousands of reports and dashboards that are consumed by internal and external stakeholders. It is important to define report sharing logic (who should access what reports and what data), overall report access management and make such reports available via a user-friendly interface. If we go by a Techie’s approach to developing the Solution in one go, it can take several months to develop any tangible solution. Moreover, BI solution keeps evolving over a period of time and multiple users/stakeholders will often ask for iterations and changes. These changes also need changes in some or all of the above activities highlighted. Given this complexity, the larger question is how should the Financial/accounting / professional services firm go about creating Customer-centric Analytics solutions? Why not focus on getting “the end results”? Let’s understand this with an example: One of the first steps is to understand and develop a custom design of the Dashboard which can be beneficial for your clients to make critical decisions. Assuming that you are supporting your Clients in FP&A, the obvious key metrics could be — Revenue, EBITDA, Gross Margin, Free Cash Flows, Trend Analysis, Variance Analysis (Budget v/s actual, Actual v/s Prior period) etc. A management consulting approach is needed to design it. The next step is to get data from various silos (accounting system, CRM solutions, excel spreadsheets) into a single centralized DataMart (viz. on Azure Cloud). Now, this looks quite technical, right? But essentially, this simply means that you get all your data in one place. You can simply get Excel/CSV extracts from relevant data sources and then connect the same with a flexible visualization tool viz. Power BI. Share this proto-type report with your clients and iterate. Once the report is fairly developed and approved by stakeholders, one can then develop the detailed data-mart. This is a much smarter approach where the focus is to get tangible results for companies quickly who don’t have the luxury to wait for a longer period of time. Moreover, this also eliminates the complexity of implementation and enable productivity. This way your firm can start monetizing Data and create a service proposition which can add tremendous value and differentiate from other service providers in the same industry. Follow an Iterative Approach: At Aays, we have divided the entire Analytics journey as a 3-phase iterative approach (a) Analytics Piloting: This is a phase where you / customer don’t wish to commit significant budgets and large-scale developments as there is not much clarity. At this stage, the best strategy is to first take an excel data dump from the various source system and connect with a visualization tool to showcase prototype reports/dashboards. This phase is just to create interest within the target customer without committing larger budgets. (b) Analytics at Scale: In this phase, there is clarity and certainty of how the dashboards will be designed and the data sources are well identified. Accordingly, the key focus is to develop the backend data architecture and all necessary processes to source data dynamically so that the key metrics of businesses are auto-updated. (c) Analytics Democratization: Once the key stakeholders (C-suites) are in line, the next phase is to ensure that even the business analysts and other staffs in the business are starting to use these reports as they have much detailed understanding of the business and their needs will further drive the next level of decision making within the firm.