How to Lower Costs for Data and Analytics Processes


Enterprises are highly aware of the fact that faster data insights hand them the edge over the competition when it comes to responding to emerging trends, seizing opportunities, and mitigating or neutralizing risks. 

On top of that, companies that enjoy deeper insights into customer preferences and behavior can deliver better customer experience (CX) which helps them win and retain more customers. 

Data analytics are rapidly becoming table stakes, rather than innovative business toys, and companies are investing in them proportionally. McKinsey estimates that spending on data and analytics rose by 47% in the period from 2019-2021, compared with 2016-2018. 

But data and analytics costs can sometimes be a step too far, especially at a time when many companies are still emerging from the pandemic-driven downturn and don’t have a lot of capital to throw around. Organizations are eager for ways to cut their data analytics costs without also lowering the results they enjoy from them. 

1. Use a data warehouse

Shifting your data storage and analytics hub to a data warehouse is one way to lower data expenses. Next-generation data warehouses help cut costs by decoupling the storage and compute functions, so you only pay for the capabilities you need at the time. When comparing data warehouse options like BigQuery vs Snowflake, bear these issues in mind.

Additionally, powerful data warehouses can run queries in a shorter time frame, thereby lowering the amount of time employees have to spend generating reports and insights. Choosing the right cloud data warehouse also delivers the added benefit of cutting costs on on-premise server hosting and data storage. 

2. Abstract analytics to the cloud

Shifting your data storage and analytics needs to a cloud-based system can give you more control over your costs. On-premise analytics systems can run very expensive, and you risk sinking money into tools that don’t get a lot of use. If you make a mistake, you could be stuck with the wrong one till the end of the contract, or end up paying for a second system at the same time. 

Cloud-based tools give you the flexibility to switch to a different option as your business needs change. Most have short contracts, so you can try out different tools until you find the one that best matches your needs, without worrying about racking up the costs. 

3. Open visibility into data analytics processes

Ironically, spending on data analytics is often highly opaque. Costs can be diffused across the organization, with IT responsible for data architecture, departmental budgets covering analytics reporting costs, and third-party data expenditure hidden within a business unit’s budget, for example. 

It’s worth it to unite data about your cross-organizational data analytics processes into a single dashboard. Once you see where your data budgets are going, you can take action on low-hanging fruit, such as duplicate licenses for analytics tools, an inflated number of third-party data streams, or individuals in different departments replicating each other’s data-gathering efforts. 

Eliminate unused or underused data feeds and trim down the number of people licensed to access each one. It’s also worth it to keep track of all subscriptions and vendor contracts, so you can reexamine costs and needs when each one comes up for renewal. 

4. Simplify data architecture

Many data teams preside over fragmented architecture that requires them to maintain an unnecessary number of data repositories and data streams, each of which costs money to host and work-hours to curate. 

What’s more, the more data repositories you own, the more data silos tend to spring up, and the longer it can take — and thus the more it can cost — to find where needed information is lying. It also increases the risk that someone will draw on the wrong data, producing unreliable reports or predictions that only amplify the costs. 

McKinsey estimates that pausing new data projects, surveying your data landscape, and simplifying your data architecture can drive savings of 5-10% within 6 months., 

5. Streamline data consumption 

Many organizations leak money on unnecessary reports that require a lot of manual processes to produce, resulting in high costs. Take a two-pronged approach by questioning how many reports you need while also automating report production. 

The more you can cut manual processes, the more you’ll lower costs. Look for easy to use self-service data analytics tools that enable employees to issue reports for themselves or view dashboards with whatever drill downs they like within minutes, without requiring a data team to put one together for them. 

6. Improve data quality

Cutting corners on data preprocessing like deduplication, verification, etc. is a false economy. Poor quality data can cost you far more in unreliable reports, mistaken decisions, and missed opportunities than you might save by skimping on data validation. 

This is one of the best places to introduce artificial intelligence (AI)-powered tools. AI can pick up on anomalies in data streams at greater accuracy and lower cost than manual oversight, and flag them so that you get earlier alerts about data issues. This way, you can cut costs on manual data management while also enjoying greater data quality. 

Data analytics doesn’t have to cost the earth

As data analytics become must-haves for enterprises across verticals, there’s no reason why they need to be accompanied by high costs. By automating data validation and report generation, simplifying data architecture, examining data analytics costs, and moving to cloud-based tools and data warehouses, organizations can enjoy the benefits of advanced data analytics without the high price tag.


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