No compromises: the data professional’s guide to increasing ROI

A challenging environment

Only 44% of data and analytics leaders globally, believe that their team is effective in providing value to their organization.

In our opinion this cannot continue – and there’s no reason why it should. But in a tough economic climate, where budget holders are scrutinizing spend and ROI ever more closely, data and analytics professionals need to be smart to prove their worth.

The same Gartner® press release reports that the second most common roadblock to the success of data and analytics initiatives is a lack of resources and funding. Meanwhile, demands from the business are increasing. According to Dresner Advisory Services 2023 ADI (Analytical Data Infrastructure) market study, while the demand for traditional BI use cases is still as strong as ever, there’s an increasing prioritization of data science and embedded analytics, indicating a greater maturity in ADI platforms and the growing demand for more predictive and or prescriptive insights. So, many teams are having to do more, with less.

How can you succeed?

Don’t compromise. Data can transform your organization, but only if it’s fast, flexible and economical. Too many data professionals are having to compromise on one or more of these elements when it comes to their analytics database. They’ll trade performance for spiralling costs or disruption to their tech stack. But they shouldn’t have to.

What you’ll learn

If you work with an analytics database on a daily basis, whether that’s part of your role as a data engineer, business intelligence analyst, database admin or any number of other related roles, we’ll guide you through three of the biggest challenges likely to be facing you in your role today. We’ll look at the issues of productivity, cost-efficiency and flexibility, exploring:

• The impact they could have on your organization
• The risks of compromising on any of these elements
• How you can overcome these challenges

Read on to fulfil the promise of your data, and your career.

How to boost productivity & scale data analytics

The challenge

The performance of your analytics database will underpin the success of your team and your organization. It can be the difference in being able to get products to market faster, reducing customer churn and gaining a competitive edge. Yet, recent data shows that 95% of businesses still struggle with operational challenges around data and analytics and 88% continue to be hindered by legacy technologies.

The problem with compromising

Far too often, data professionals are having to compromise. They are stuck using underperforming legacy databases that the business doesn’t want to touch, in order to preserve the vast amounts of money already invested. The intimidating alternative is to go through an expensive, time-consuming infrastructure overhaul to replace these outdated systems – the seemingly more attractive option is often for businesses to do nothing. In other words, they trade-off performance for cost.

Read our blog: Are legacy databases holding your business back?

^*The Total Economic Impact of the Exasol Analytics Database Study, commissioned by Exasol and conducted by Forrester

Sacrificing productivity for lower cost is a false economy – here’s how you can turn things around.

How you can win

Understand the importance of the database layer

• The database layer can transform the productivity of your Business Intelligence (BI) activity, but it’s likely to be responsible for more than that. It could be the driving force behind your other data environments and data science platforms. A new database can influence:
  • The data sources you can pull in to your BI tool(s)
  • The efficiency and speed with which your system completes Extract, Transform and Load (ETL) tasks
  • The quantity and complexity of the queries you run
• Rather than draining power and productivity from your data stack and BI tools, a high-performance database can lift your system to a new level. Make sure your budget holders know this as well

Prove value with quick wins for real-time, deep analytics

• Set up a live connection between your analytics database and the BI tools it’s serving to deliver fast, independent, flexible and deep analyses to BI end-users
• BI users will no longer have to rely on inaccurate, stale, pre-aggregated data. Instead they’ll be able to:
• Combine multiple data sets
• Define the parameters most suitable to their organization
• Discover new insights at speed
• The faster you can deliver insights, the more likely you are to earn buy-in from engaged senior stakeholders who control your budget. You’ll also save huge amounts of time for Database Administrators who can in turn focus more on value-adding tasks

Make Machine Learning actionable at scale

• While budgets are tight, there will be even greater scrutiny on the ROI delivered against ML investments
• Focus on working with an analytics database that delivers the power and ability to scale ML
• At the same time, successful ML deployment will depend on the relationship between data scientists and data engineers. Harmonious collaboration is critical to ensure that ML scoring models created by data scientists, for example, are properly integrated into production systems and processes by data engineers.

Read our 3-step guide: How to optimize your data stack and get more from your BI tools

Cost savings: how to make your budget go further

The challenge

Amidst economic uncertainty, data and analytics teams are expected to do more with less. Demands from the business for data insights will only grow, regardless of the budgets available to meet these needs. Yet far too often data teams are stuck working with legacy systems that leave them no hope of delivering what the business needs because of the assumed expense of replacing these systems.

The problem with compromising

As business departments demand more insights, the data volumes you’re working with, the concurrency required and often the complexity of the workloads, can all increase. That makes it essential for you to be clear on how much this will cost. Can your existing analytics database scale cost-effectively? Can it handle more advanced analytics challenges? Crucially, do you know where to run certain analytics workloads within budget?

Read our blog: How a lack of data strategy is killing your ROI

In the search for speed and agility many organizations will migrate data workloads to the cloud, but very soon these businesses can be hit by surprise with spiralling costs. But if you have a clear understanding of your data workloads and where you are running analytics, you shouldn’t have to accept extortionate costs in return for higher productivity and a more flexible approach. Nor should you have to continue to sink money into legacy databases that are the data bottlenecks behind inaccurate, stale data, just because stakeholders are wary of a disruptive rip-and-replace.

How you can win

Understand where certain data workloads will fare best

• If your remit is to handle variable data workloads or manage bursts of data analysis then the Pay-As-You-Go model of the public cloud could be your most cost-efficient option. But this doesn’t have to be your default option. If you’re looking to work with resource-intensive data warehouse workloads that require substantial data usage and analytics use cases, your costs can quickly rack up in the cloud. So, you need to have a clear view of the workloads you intend to migrate before committing to the cloud.

Make sure you’re aware of the costs involved when making a deployment decision

• Cloud Service Providers charge transfer fees for transferring data out of a public cloud. You need to be clear what these rates are and the costs you are likely to incur. If these costs come as a surprise, the advantages of a hybrid approach for example, could soon be negated in the eyes of budget holders. Bear in mind that CFOs are quickly having to become more cloud savvy so the more on top of this you are, the better your chances of securing budget

Opt for a simplified, automated database layer

• A mature data layer might include solutions from multiple vendors and that means managing a lot of contracts. And where there’s administration, there’s cost. By comparison, a simplified and automated database layer has lower overheads for tuning and hardware thus reducing the total cost of ownership (TCO).

Read the Forrester TEI Study: Find out what cost-savings and ROI are possible in the, ‘Total Economic Impact of the Exasol Analytics Database’ conducted by Forrester on behalf of Exasol.

Flexibility: how to choose the right deployment strategy and avoid infrastructure overhaul

The challenge

According to 451’s DataOps Dilemma Survey, two of the top five technology-based limitations contributing to data ‘supply side’ bottlenecks were ‘connectivity or integration challenges’ and ‘cloud compatibility challenges’. So many organizations are being held back from doing more with their data because they’re spending so much time and money building workarounds for an analytics database that doesn’t integrate with their data stack and analytics ecosystem, or are undergoing a painful rip-and-replace. As a result, they’re never going to fulfil the promise of the data at their disposal.

^*DataOps Dilemma: Survey Reveals Gap in the Data Supply Chain, Paige Bartley, Senior Research Analyst, Data Management, August 2021

Read our blog: 6 questions to ask when choosing where to deploy your analytics database.

The problem with compromising

For organizations chasing faster query processing times or lower costs, there’s a very real risk of sacrificing flexibility and being landed with an analytics database that further disrupts what could already be a very complex architecture. The more manual integrations you add between databases, the slower, more expensive and less reliable your analysis becomes. What’s more, you could very easily fall victim to vendor or platform lock-in and lose control over data ownership – this could lead to significant fines and reputational damage for organizations operating in industries where regulation dictates where they manage certain data workloads.

How you can win

Run analytics where your data lives

• Make sure you’re clear on which data workloads you want to move to the cloud and which may work better on-premises
• Check whether you have significant compliance obligations and operate in a highly regulated sector that dictates that data must stay within certain locations or jurisdictions. You will need an analytic database that will allow you to run analytics wherever your data lives, not just in the cloud

Change your architecture gradually – you can succeed without a rip-and-replace

• The architecture of most enterprise-sized organizations is likely to be made up of years of tech procurement, start-stop adoption, inherited systems and complicated integrations. Just mapping your current data stack might be challenging and that’s before you can even contemplate swapping out the fundamental database layer. Yet doing nothing presents more of a risk – it might result in your BI tool(s) falling short. And this means someone, somewhere, is making better and faster decisions than you. So rather than regarding it as an enormously complicated upgrade, look at it as a gradual journey and start small.

Choose an analytics database with a high level of built-in integration and automation

• An analytics database’s level of built-in integration and automation with ETL tools and individual data sources is critical. This reduces TCO significantly. It also creates a flexible playground to pick and choose the specific tools you want. You can move from a rigid structure where choice is restricted and improvements slow to execute, to a fluid and adaptable stack.

Read our step-by-step guide: Make the right deployment choice for your analytics database.

What’s next: there’s no need to compromise

You deserve an analytics database that increases productivity, cost-savings and flexibility, without any trade-offs, so your businesses can act on real-time insights to drive competitive advantage.

Enter Exasol.

Exasol is the only analytics database to deliver on all three of these critical elements, without forcing you to compromise. Exasol customers get data insights up to 20x faster, and can achieve ROI of more than 300% with reduced licensing, implementation, maintenance and training fees, eliminating cost shock and vendor lock-in. With Exasol, businesses have flexibility to manage data the way they want – in the cloud, SaaS, on-premises, or anywhere in between.

Read our ebook: Learn more about how Exasol can transform your ROI