"Garbage in, garbage out" is most applicable in the realm of data, emphasizing that if the input data is not clean or reliable, extracting meaningful insights becomes a formidable challenge.
Bad data costs the US $3Tn every year according to Harvard business review.
We have collaborated with numerous companies to comprehend the hurdles they encounter in maintaining data quality and to identify processes and best practices that can be implemented to ensure their transformation into a data-oriented entity. The efficacy of a tool is inherently tied to the quality of the data it receives. In the upcoming articles, we will delve into various stages of companies and diverse data sources where maintaining data hygiene is crucial to being a position in the top 1% in terms of taking data backed decisions, particularly for SaaS and consumption-based companies.
Different Stages of Companies:
1. Early Stages (0-1 Mn ARR):
- Most operations occur in Google and Excel sheets.
- Foundational systems like CRM and accounting tools are being established.
- Not all activities are being recorded in the system.
Neglecting data accuracy at this stage can lead to significant issues in subsequent stages. Companies should start addressing this concern from the early stages.
2. Early Mid-Market (1-30 Mn RR):
- Challenges arise as companies scale, and data volume increases.
- Correcting data becomes progressively difficult due to a variety of errors.
- Systems are set up but often contain inaccurate data.
It is crucial to take a step back at this stage and implement processes to ensure accurate data recording and reporting.
3. Late Mid-Market (30-100 Mn ARR):
- Systems are in place, and processes such as deal desks are established to ensure data quality.
- Managing different sources of truth becomes a challenge, requiring alignment among all stakeholders.
Different Systems:
- Marketing Data Source: Many input this data into the CRM or use a separate system, with HubSpot being a common choice.
- CRM (Customer Relationship Management): Central to every organization, containing customer data and components like deal desks and CPQ.
- Billing Software: Often a messy system, with some companies still using Google Sheets/Excel for invoicing. Lack of systems accommodating unique business models poses a challenge.
- Accounting Tool: Complications arise when using different tools for different entities until transitioning to an ERP.
- Customer Success Tool: Integral for ensuring customer satisfaction.
-Usage Data: Crucial for understanding how customers interact with products/services.
A common challenge across these tools is the timely updation of data, and incentivising folks in the organisation to keep it up to date. Next up, we dive deep into a featured case study on how one of our customers, Scrut Automation was able to get upto 95% Data Accuracy across their CRM system.
