When Spreadsheets Stop Working: A Beginner’s Look at Data Warehousing

When Spreadsheets Stop Working: A Beginner’s Look at Data Warehousing

Gone were the days when spreadsheets like Excel were used by many small and medium-sized businesses as a tool for storing and analyzing data. Spreadsheets are simple layouts with basic calculation abilities and seem like a simple solution. The only problem with this approach, however, is that as companies grow and the volume of data gets bigger and bigger exponentially, the spreadsheets hit their breaking point. They become slow and unstable because they are formula-heavy. Multiple data copies increase the likelihood of data integrity errors since data needs to be manually updated. The analysis without cross-functional analysis is limited.

If business spreadsheets are not working well, it’s time to move business data to a more mature platform: the data warehouse. Although the term sounds too technical, understanding core concepts through data warehousing services & implementation makes the topic far more approachable for beginners. With some background knowledge, SMBs can plan and execute an accessible data warehouse that is appropriate for business insights and better decisions.

The Basics: What is a Data Warehouse?

A data warehouse is a repository for consolidated data from different sources and structured for querying and analysis. It pulls data from across an organization into one place to be a “single source of truth” for business users to explore.

Unlike a spreadsheet’s simple two-dimensional layout, data warehouses employ multidimensional data modeling. This allows users to access the exact data points required for diverse analysis needs. Common warehouse architectures include star schemas, snowflake schemas, and data vaults.

Warehouses may be on-premise or in the cloud. Leading cloud data warehouse platforms include Snowflake, BigQuery, Redshift, and Azure Synapse Analytics. Wherever they reside, data warehouses share key capabilities that deliver value:

  • Store and analyze high volumes of data while sustaining performance
  • Allow diverse groups to access data for different needs
  • Analytics power. Enable complex analysis with extensive query options
  • Data integrity. Provide accurate, integrated data in one reliable location

Top Reasons Spreadsheets Stop Working

Spreadsheets are convenient analysis tools for basic business data. But they falter beyond a certain scale for many reasons:

Data volumes exceed spreadsheet capacity

Spreadsheets have size limitations around data storage and processing that make them unwieldy for enterprise needs. For example, Excel sheets can only handle about a million rows and 16,000 columns per tab. Performance slows significantly beyond those points.

Manual processes spawn errors

With spreadsheets, users must manually import data from multiple sources, match data points across sheets, and consolidate reporting. This hands-on work causes frequent human errors in data inputs and formulas. Over time, data accuracy and integrity decline.

Collaboration is difficult

Spreadsheets began as personal productivity tools, not collaborative solutions. They lack built-in features to handle user permissions, audit tracking, change logging, and version control. Simultaneous editing often corrupts files.

Analytics are limited

While formulas can calculate metrics like sums, averages and variance, conducting complex statistical analysis in spreadsheets is very difficult. Pivot tables can assist, but their capabilities are limited for advanced analysis without coding knowledge. Spreadsheets just weren’t built for cutting-edge analytics.

Scalability is non-existent

As companies expand, spreadsheets can’t easily scale up to handle exponential data and user growth. At high volumes, file performance slows to a crawl. Cross-functional data integration is extremely difficult across thousands of disparate sheets.

Those structural limitations make spreadsheets fragile solutions as organizations and data mature. Breaking changes demand investment in more capable, lasting platforms built specifically for enterprise data and analytics.

Data Warehouse Adoption Is Growing

The data warehouse landscape has advanced tremendously in recent years. Solutions have moved from on-premise appliances to cloud-based services that are simpler and more accessible for businesses of all sizes. Concurrently, data warehouse adoption has soared:

Market leader Snowflake grew revenue by over 120% from 2019 to 2020. Their IPO saw the largest software valuation in history. This underscores intense business demand for more analytics power. The data warehouse revolution has reached a tipping point, especially following pandemic disruption.

Preparing to Adopt a Data Warehouse

Jumping from spreadsheets to centralized data warehousing represents a strategic leap. Companies shouldn’t blindly rip and replace without thoughtful preparation and planning:

Build internal skills

Data warehousing requires new technical expertise like database architecture, ETL (extract, transform, load), and SQL queries. Seek out resources for warehouse education across leadership and analytics teams.

Assess data readiness

Examine current data health across formats, integrity, governance, and pipelines. Identify action areas to clean up and consolidate data sources and flows.

Define key business goals

Clarify top business objectives and analysis needs. This focuses warehouse design conversations to enable the right insights. Don’t shoot too wide or get lost in technology possibilities.

Start small, deliver value

Early warehouse initiatives often fail by biting off too much change. Focus the first phase on a manageable but high-impact business area to demonstrate wins.

Plan for data growth

Volume, users, and use cases will all expand down the road. Seek adaptable platforms and modern methodologies like agile development to accommodate future needs.

The Cloud Data Warehouse Advantage

Once ready to make the data warehouse move, SMBs should strongly consider cloud-based solutions rather than on-premise options. Cloud platforms provide faster setup, simpler administration, flexible scaling, and availability of advanced capabilities like machine learning.

Snowflake, BigQuery, and Azure Synapse make cloud data warehousing radically easier to approach. The cloud alleviates the major barriers to entry through using guided onboarding, preconfigured templates, and low administrative needs. It can be used by even smaller teams to onboard and see value quickly.

Snowflake shines as the current market leader, growing revenue over 120% year-over-year. With a unique cloud-native architecture, Snowflake delivers extreme flexibility, scalability, and near-endless concurrency for any type of data use case. Their platform-as-a-service model also offloads all infrastructure management, reducing ownership headaches for customers. Snowflake’s ease, power, and growing partner ecosystem explain its rapid rise to prominence.

Getting Started: First Data Warehouse Steps

Transitioning from spreadsheets is an enormous change. The best approach is to start small, act strategically, and iterate. For the first warehouse initiative, businesses should:

  • Identify a manageable starting data domain aligned to the top business goals. Don’t take on the entire enterprise at once.
  • Select technology for speed and simplicity – a leading cloud data warehouse like Snowflake running on cloud infrastructure like AWS or Azure.
  • Build a small but cross-functional warehouse team with business and technical contributors. Key roles include data engineers, ETL developers, database architects, and business analysts.
  • Clean up and model the initial data domain for warehousing – plan ETL processes and define schemas.
  • Load the first datasets and validate with business users via basic SQL queries and dashboard visualizations.
  • Provide self-service access to let more teams derive insights from the new integrated data.
  • Capture lessons learned to guide expansion initiatives across more data domains.

A Bright Future for Data Warehouses

Simple analysis tasks will always find a place in spreadsheets. However, even with the amount of data they have, businesses are being forced to migrate to enterprise-grade platforms built to address flexibility, scale, and highly impacted analytics. SMBs are now able to adopt data warehousing at lower risk and cost than ever before by starting small with the cloud. Today, what feels unfamiliar will become one of the must-have pillars of data-driven decision-making. A future with the spreadsheets not working is a future with exciting new opportunities based on warehouse data being used strategically.

 

An original article about When Spreadsheets Stop Working: A Beginner’s Look at Data Warehousing by Kokou Adzo · Published in

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