Creating a Trusted Foundation for Data Analytics

Exploring the need for databases in your business.

There is no denying many businesses rely heavily on spreadsheets like Microsoft Excel or Google Sheets. Spreadsheets are excellent tools for specific tasks—they are user-friendly, flexible, and familiar to most people. However, as your business grows and data management becomes more complex, relying solely on spreadsheets can lead to inefficiencies. This is where databases come into play.

Spreadsheets

Spreadsheets are grid-based files designed for organizing, calculating, and analyzing data. They are particularly useful for:

• Simple Calculations: Performing basic arithmetic and statistical operations.

• Data Organization: Keeping track of lists and simple records.

• Quick Analysis: Creating ad-hoc reports or visualizations.

"Spreadsheets may be limiting the way your business runs"

1. Scalability: Spreadsheets can become cumbersome and slow when dealing with large datasets.

2. Automation: Minimal data validations and high manual effort to constantly update formulas increases the risk of errors, leading to inaccurate analysis.

3. Security: Lack of security features, access controls and in many cases no version control creates significant security risks.

4. Functionality: As business complexity increases things become harder to manage and maintain organized from a single spreadsheet.

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Databases

Databases, on the other hand, are designed to handle large volumes of structured (and unstructured) data efficiently. They are useful in storing data from all areas of a business in an organized and secure manner.

• Scalability and Performance: Databases can handle vast amounts of data without performance degradation. They are optimized for quick data retrieval and updates.

• Integrity: Databases enforce field integrity ensuring data collected is accurate.

• Joining: Databases facilitate efficient data merging, simplifying the process of joining information from multiple sources.

• Security: Databases offer robust security features, they can be hosted in the cloud, include user authentication and access controls to protect sensitive information.

"Being able to write SQL queries allows extensive manipulation of datasets in a consistent and reliable way"

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This Is Only The Beginning

Data collection is only the first step in the analytics maturity curve.

This is the foundation for any data centric organization and it starts to open the doors to new ways of operating and looking at your business.

• Data Warehouse: A data warehouse allows data storage across all areas of a business. This can include data from an ERP, CRM, accounting software, sales tools or any other specialized systems in place. It can also store external files that lived once in spreadsheets or unstructured data coming from PDFs or text files.

Example

Being able to automatically collect sales data from multiple channels (internal, website, e-commerce sites, inventory / warehousing information, internal benchmarks, social media.)

Creating a centralized location for data eliminates wasted time looking for information and understanding the truth amongst conflicting versions.

• Business Intelligence: Interactive dashboards and reports allow access to the information stored in the databases or data warehouse. These tools allow users to explore the data on their own, find insights, view pre-calculated KPIs and share those insights in a concise manner with a large audience. The information can be be refreshed automatically as new data is collected.

Example

Being able to easily visualize total sales across all platforms in a consolidated view knowing the data is always updated.

Interactive dashboards can help managers explore sales to find trends in seasonality, combine sales data with inventory levels and monthly attainment targets, measure different KPIs and share those insights with their team without repetitive manual effort.

• Integrations: Sometimes visualizing the information is not enough. Sometimes we need our apps to communicate with each other. Through automation, we can interact with the data collected and trigger actions that make these tools communicate with each other.

Example

When a new order is received we can share automated emails with a client, add them to a CRM and ensure follow-ups are sent at the right time.

Another example might be inventory replenishments - managers can interact and edit replenishment thresholds. When a product meets this criteria, it can automatically trigger a re-order workflow in the company's ERP (SAP for example).

• Artificial Intelligence: Advancements in AI capabilities make it easier than ever to integrate into every-day processes. They are helpful in detecting hidden patterns, forecast trends and automate manual tasks amongst other things.

Example

We can receive insight recommendations such as inventory replenishment schedules based on detected patterns. 
Perhaps pricing adjustments that will increase revenue while maintaining demand.

It can help to extract keywords from product reviews that can improve analytics capabilities and drive changes in marketing.

It can help new team members ask about company policies (stored in lengthy PDF documents) or even answer customer questions to improve overall service.

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Decision Lifecycle: Analyze → Act → Analyze Again

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Getting There

Many organizations are attracted to new things like artificial intelligence or machine learning. While those initiatives are important it is perhaps more important to build a strong foundation that can scale and allow those initiatives to succeed in the future.

Spreadsheets still have their place. For small-scale, straightforward tasks, spreadsheets remain a practical and convenient option. However, as your data needs grow, thinking about leveraging a database system can provide the scalability, efficiency, and reliability required to support your business operations into the future.

Starting this journey might seem daunting, but the long-term benefits far outweigh the effort.