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How self-service analytics can solve your productivity struggles

“Personal solutions can be useful, but the most effective antidote to low productivity and inefficiency must be implemented at the system level, not the individual level.”

Daniel Markovitz, author and consultant

Productivity is a key organizational focus — and understandably so. Improved productivity means getting more from your available resources, leading to improved profitability and higher margins.

But your attempts to improve productivity cannot just focus on individuals. Yes, productivity is about how effective your people are in their roles — but that effectiveness is determined by the systems you put in place to support them.

Solving your productivity challenges means empowering your teams to focus on their most impactful work and reducing the impact of repetitive manual tasks. Doing so will enable your teams to stay engaged without being overburdened — but how can you achieve this?

Embracing self-service analytics is a key part of the solution.

What is self-service analytics?

Typically, your organization’s data processes are split into two categories:

Complex, technical processes carried out by your IT team. This could involve sophisticated analyses of large datasets or data cleansing and validation processes.
Outcome-focused tasks performed by business users. Think marketing managers reporting on campaign metrics or senior leaders evaluating resource allocation.

In many cases, the latter have to rely on the former to support their data needs. Depending on how your organization is structured, business users may need to lean on IT for everything from basic data access to preparation and analysis tasks.

But there’s an obvious trade-off here. The less business users can do on their own, the heavier the burden on your IT team. They’ll spend more time on data support tasks and less time on their key deliverables. By the same token, business users will find their own work delayed as they’re forced to wait for the support they need.

Self-service analytics is a way to avoid this trade-off. In simple terms, self-service analytics empowers business users to work with data directly.

There are prerequisites to delivering self-service analytics, of course. Your business users will need to be able to access reliable, up-to-date data on demand and automate data preparation and reporting tasks. Once these elements are in place, self-service analytics will allow them to get the insights they need without additional support.

Adopting self-service analytics offers extensive productivity benefits. Let’s look at what you can expect.

The productivity benefits of self-service analytics

1. Reducing workloads

This may seem counter-intuitive when we’re talking about productivity. Don’t we want our teams to be doing more, not less?

But this highlights a common misunderstanding. More work is not always better. If the work is low-stakes, repetitive, and lacks a clear business value, then you should be doing your best to reduce it — or to avoid it entirely.

And this perfectly describes the kind of repetitive data tasks that self-service analytics eliminates. Experienced developers fielding data access requests, senior managers manually filling out the same report every single week… These are diversions from business-critical tasks. They mean higher workloads and more stress for little reward.

By enabling self-service analytics, you’ll empower business users to focus on their essential outputs. The first step is to ensure that your data is cleansed and validated in advance — using automated data validation processes, for instance. This will allow business users to begin their preparation and analysis without needing to spend time checking the data is correct — or reaching out to IT for support.

2. Accelerating data access

To deliver in your role, you need access to key data — and you need it now. With data on hand, you can make better decisions, develop critical insights, and react at pace to changing circumstances. It should come as no surprise that data-driven companies see improved profitability, market share, and customer satisfaction.

But if accessing data means a lengthy back-and-forth across multiple email chains, you’ll find yourself waiting around for the data you need. And if your organization struggles with data silos, you may not even know what data is available in the first place. You’ll have to take on some extra-curricular detective work before you can get down to your actual tasks.

All that time and energy diverted from key deliverables means a big hit for your overall productivity — and more frustration in your day-to-day workload.

Self-service analytics begins with access to validated, curated datasets. At its core is a live data catalog, which allows business users to see exactly what data is available and access it immediately. You’ll spend less time chasing the data you need, and more time on engaging work.

3. Limiting data preparation time

How easy is it to make use of the data that lands on your desk?

Perhaps you need to filter a master dataset to get the information you need. Or maybe you get datasets from multiple teams, each using different formats for key fields. Either way, you’ve got a great deal of manual data preparation to get through before you can start on work that delivers real value. And if you’re faced with the same prep tasks every single week, you’re losing significant blocks of time on work that doesn’t make use of your full skillset.

With the right tools in place — and with access to cleansed and validated datasets — self-service analytics can enable business users to record and repeat their data preparation workflows. What was once an hour or two of tedious work is reduced to a single click.

4. Preventing data errors

You won’t be surprised to learn that errors in your datasets can prove costly. In fact, data quality issues cost companies $12.9 million per year on average.

But the costs of bad data go beyond the direct impact on your decision-making. It’s also about the time spent reversing those decisions. Even the smallest of errors incur an opportunity cost, taking up time that could have been put to better use.

By supporting business users to engage with data regularly, self-service analytics can significantly improve your overall data quality. Cutting down on manual tasks means less chance of simple errors creeping in.

Supercharge your productivity with data self-service

“The single greatest challenge facing managers in the developed countries of the world is to raise the productivity of knowledge and service workers. This challenge, which will dominate the management agenda for the next several decades, will ultimately determine the competitive performance of companies.”

Peter Drucker, management consultant

Peter Drucker wrote those words in 1991. The past thirty years have proven him right. Productivity is more essential than ever to maintaining a competitive advantage, with productivity leaders performing 5.4x better than those falling behind.

When it comes to securing the advantages of high productivity, self-service analytics has a key role to play. But to implement self-service analytics effectively, you need the right tools in place.

CloverDX is a data integration platform built with business users in mind. Its Wrangler feature allows your business users to explore no-code data transformations and build their own custom automation with ease. And it’s tightly integrated with the data catalog, which supports self-service data sharing.

To find out how CloverDX can transform your organization’s approach to data, book a live demo with one of our experts.

Read MoreCloverDX Blog on Data Integration



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