One does not simply adopt data analytics solutions on a whim.
Data analytics newbies might struggle to aggregate the right information across a company’s IT infrastructure and prepare it for further analysis. SaaS analytics tools can be hard to customize and scale across multiple use cases. And empowering every employee, regardless of their technical background, to use analytics insights in their daily work is a task few businesses succeed in.
These technological and organizational challenges, coupled with the presumably high cost of data analytics, slow down analytics adoption in enterprises.
We’ve already told you about the obstacles companies face when attempting to boil Big Data down to meaningful insights, so let’s talk about data analytics cost today.
Key factors affecting the cost of data analytics
- Amount, nature, and quality of your data
- Your data analytics objectives — and tools that best meet them
- Data analytics vendor fees
- Data analytics software customization and development efforts
- Overall agility and willingness to change
Factor 1: Amount, nature, and quality of your data
Back in 2016, the average company was managing 162.9TB of data; in enterprises, the data volumes typically exceeded 347TB.
Six years on, companies see their data grow by 63-100% EVERY MONTH. The median number of data sources per company has already reached 400, while 20% of companies surveyed by IDG and Matillion last year claimed to be pulling information from over 1,000 sources to meet their business intelligence (BI) and data analytics needs. These sources often span eCommerce platforms, customer databases, project management software, social networks, and IoT systems, among others.
Analysts estimate that 80-90% of business data is unstructured. This data comprises images, videos, audio files, uneditable documents, and sensor readings, so you cannot store it in traditional relational databases.
Unlike structured data, which resides in on-premises or cloud-based data warehouses and is ripe for BI tools and dashboards, unstructured data belongs to data lakes and data lakehouses. Since data lakes do not possess computing capabilities by default, companies should meticulously prepare unstructured information for streaming analytics, data warehouse-assisted analysis, or AI-driven algorithmic processing.
That’s why the cost of data analytics includes the expenses associated with devising an end–to-end data management strategy, setting up a complete data storage infrastructure for company-wide data aggregation, and bringing unstructured data to a unified format. Overall, these expenses comprise one-third of the total data analytics cost.
Factor 2: Your data analytics needs — and tools that best meet them
Depending on your company’s digital maturity and stage of development, you might need tools for:
Descriptive data analytics. When you dig into historical data to determine what has happened, when it’s happened, and why, you’re tapping into descriptive analytics. Essentially, it’s the backbone of all business intelligence and reporting tools, which can provide ad hoc or canned reports. The former give answers to particular business questions — for instance, how many users clicked on your banner ad. The latter can be a monthly report produced by your social media or PPC specialist.
Diagnostic data analytics. This type of analysis involves matching historical data against other information to figure out why something has happened and what you can do about it. If your sales managers closed abnormally few deals last month, you can turn to diagnostic analytics to boost their performance in the future. For this, two techniques can be applied:
The first approach is called query and drill downs. Such analysis identifies the root cause of an event. Going back to our sales department example, the reason for your managers’ lackluster results could be long-term sick leaves.
Discovery and alerts is the second approach. This technique allows you to catch early signs of a problem and take action before the disaster strikes. For instance, a data analytics platform could notify you about sales managers’ prolonged absence from work in the middle of the month so that you could double your business development efforts.
Predictive data analytics. With predictive analytics, your company could spot correlations between certain events and identify trends — for example, anticipate sales volumes based on the target region and audience demographics. To that end, data specialists turn to statistical and predictive modeling:
Statistical modeling helps find dependencies between different parameters — say, geography and revenue.
With this data, it is possible to further calculate sales for various target audiences in a particular region — and that’s where predictive modelingcomes in useful.
Prescriptive data analytics. Powered by Big Data and artificial intelligence algorithms, prescriptive analytics tools not only notice recurring patterns and identify root causes of events but also recommend the best course of action to avoid problems, boost employee productivity, and reduce your company’s operating costs. To make intelligent predictions, your data analytics platform will implement the optimization and random testing techniques.
Subsequently, we need different tools to perform those types of analyses — and data analytics costs will increase proportionally with the platforms’ feature set, complexity, and integration capabilities.
According to Vitali Likhadzed, ITRex Group CEO and Co-Founder, we could segment modern data solutions into three categories:
Standalone tools and utilities. This category includes open-source products like Apache Kafka and RabbitMQ and SaaS offerings like Tableau and Power BI. These tools solve a particular business or technical task — e.g., visualizing operational data, ensuring data exchange between IT systems, preventing transaction fraud, or locating misplaced inventory using RFID tags. But from a company-wide data analytics implementation perspective, such tools constitute only one building block of a more complex system.
Industrial SaaS platforms like SAP, Snowflake, Salesforce, and TIBCO Spotfire. These systems provide a single platform for managing operational data. Thanks to a wide selection of modules and settings for specific scenarios, use cases, and entire industries, industrial platforms can cover most of your company’s data analytics needs. Salesforce’s Einstein, for example, even makes intelligent predictions based on historical and real-time information it scavenges across your company’s IT infrastructure. The problem is, such systems might be too tricky to customize — or lack out-of-the-box modules for your industry. When it comes to data analytics cost, implementing SaaS platforms may also become too expensive over time as your business grows and starts generating more data — after all, most SaaS vendors’ plans are tied to storage and computing resources.
Integrated enterprise-wide data ecosystems that incorporate industrial SaaS, open-source, and bespoke data analytics tools. Such systems aggregate data across your IT infrastructure and are well-suited for company-wide analytics. You could also spice up your data ecosystem with artificial intelligence capabilities to spot recurring patterns in both structured and unstructured data, anticipate events and scenarios that could affect your business, and automate tedious tasks. This helps unlock new opportunities and revenue streams, thus maximizing return on your data analytics investments. Custom data systems are extendible and tailored to your processes, business functions, and usage scenarios. Although they might be vendor-agnostic by default, in reality, companies need to take certain steps to reduce their reliance on a particular cloud provider, which might eventually increase data analytics costs.
This cohort of tools offers comparable data analytics capabilities. Implementation costs aside, the key drawback of standalone and industrial SaaS offerings is their lack of flexibility.
To be precise, Tableau and Power BI boast reasonable flexibility within one department or set of tasks. And data analytics solutions powered by industrial SaaS platforms are vendor-locked; should you decide to switch to another platform in the future, you’ll have to rewrite the whole thing from the ground up and invest millions of dollars in employee training.
Integrated data ecosystems, on the other hand, can be costly to implement, but offer utmost flexibility, meet industry standards, and best suit your needs.
Factor 3: Data analytics software pricing
If you opt for a ready-made data analytics solution, which may need either little or extensive customization, your data analytics costs will incur vendor licensing fees.
Let’s see how much popular data analytics tools cost — and what they offer functionality-wise:
|Microsoft Power BI||
The tool’s functionality varies depending on the actual plan:
In Tableau, data analytics cost would again depend on the functionality of the chosen plan:
The TIBCO Spotfire data analytics pricing depends on the product you’ll choose:
*billed monthly or annually
|SAP BusinessObjects Business Intelligence Suite||
The SAP BusinessObjects BI Suite data analytics pricing information is only available at request.
According to Capterra, it’ll cost your company at least $14,000 per year to tap into Big Data analytics with SAP.
The former comes with robust functionality for sales and marketing analytics:
If you’re considering using Salesforce CRM Analytics, there are four products to choose from:
Let’s do the math, shall we?
If your organization opts for an advanced solution like SAP, your data analytics initiative could cost you at least $14,000 monthly — and here we’re talking about vendor fees only! Additional expenses might include the purchase of complementary SAP products, software configuration, and employee training.
According to a survey conducted by 1PATH, 46% of small and medium-sized businesses spend anything between $10,000 and $25,000 to purchase a data analytics tool, while 41% of the respondents pay an equal amount in maintenance fees annually.
Even though most vendors are well-aware of the data analytics cost problem and attempt to democratize their products, it still takes companies around four months to implement a standard BI solution and start benefiting from it.
Factor 4: Data analytics software customization and development efforts
Data analytics tools are not created equal.
Some plug-and-play solutions, such as Power BI and Tableau, can produce quick insights without extensive customization. Their intelligence, however, seldom stretches beyond descriptive and diagnostic analytics and cannot be scaled company-wide.
More advanced tools like SAP and Salesforce could empower you to source, aggregate, process, and analyze data across your entire organization — but it’s easier said than done.
Let’s take Salesforce as an example. Although the platform offers a rich selection of add-ons and tools to sync it with your IT infrastructure and third-party services, you could easily spend $10,000-100,000 to tweak the tool to best suit your unique business needs.
That’s why some companies choose to create bespoke data analytics tools around their data, processes, and goals — even if the approach will involve higher upfront investments.
How much would a custom data analytics platform cost your company should you choose this path?
Case study: ITRex creates an AI-powered self-service BI solution for a leading retailer
Following a discovery phase, we outlined the project scope:
Break down the silos between the company’s disparate technology systems to enable uninterrupted data sourcing and aggregation
Detect, modify, and delete inaccurate, incomplete, or irrelevant data across the company’s IT infrastructure
Create a Master Data Repository serving as a single source of truth for all organizational data
Develop a web portal providing a 360-degree view of all the company’s data sources and information available in different formats — PDF files, documents, Excel spreadsheets, emails, images, etc.
Build a self-service BI platform to empower users, regardless of their technology background, to interpret data insights and create ad hoc reports
Implement advanced security and role-based access control mechanisms
We created a data ecosystem spanning several innovative features and technology components:
Node and edge-driven graph data structure that supports complex queries and simplifies algorithmic data processing
Effective search through massive volumes of data with the Hashtag Search and Hashtag Autocomplete functionality
Integration with third-party systems via a custom API
Integration with different applications and systems comprising the customer’s IT infrastructure: Office 365, SAP, Atlassian products, Zoom, Slack, and enterprise data lake, among others
Option to create and share detailed reports by querying numerous data sources
Built-in collaboration tools
Role-based security mechanisms restricting access to sensitive information stored in graph databases
The data analytics platform can handle up to eight million queries per day, serving the needs of non-technical employees who previously had to request reports from the client’s internal IT teams.
The platform’s core advantage is its flexibility and scalability across use cases. Whether our client needs to produce a financial report, gain an insight into consumer behavior, or adjust their pricing strategy, the system can do it all.
For instance, the data ecosystem helped the company reduce operating costs by advising on whether they should repair or replace pieces of equipment and other assets — and that’s just one of its possible applications.
Vitali Likhadzed estimates the cost of developing and implementing a data analytics solution like this at $150,000-200,000 — and we’re talking about a very basic version of the system here. It’s possible to roll out a minimum viable product (MVP) in three months, while full-scale implementation would take six to nine months on average.
Factor 5: Overall agility and willingness to change
Broadly defined as a company’s ability to quickly respond and adapt to changing market needs, organizational agility plays a pivotal role in data analytics implementation.
While not directly related to data analytics cost, organizational agility (or lack thereof!) will affect your data solution deployment timeline and, subsequently, time to value.
A recent survey by Exasol indicates that 65% of organizations have experienced employee resistance when adopting data-driven methods in their work — even though 73% of the respondents initially believed they would not face such obstacles. Among the reasons for employees’ reluctance to use data analytics systems are a lack of understanding of a company’s overall data strategy (42%) and limited knowledge of data analytics benefits (40%).
If your supply chain management company has been using Excel spreadsheets for 25 years, you can’t force your employees to start using Tableau, SAP, or a custom self-service BI system without proper onboarding — and learning takes time. Employee training costs aside, you will also have to part with some of your employees and hire tech-savvy ones.
How to get started with data analytics, reduce costs, and achieve payback faster
Considering the high data analytics cost and extended project cycles, you’re probably wondering whether you should tap into Big Data analytics in the first place.
Let’s get something straight: nowadays, becoming a data-driven company is no longer a compelling, albeit elusive, advantage — it’s a key to survival in the digital economy, where the divide between data leaders and data laggards will become unbridgeable in just three years.
The good news is, you can reduce the cost of implementing data analytics and reap its benefits faster by following the steps below:
Define the mission and goals of your organization with regard to digital transformation. When doing so, leverage the research and planning frameworks like PESTEL/TEMPLES, VRIO, Porter’s Five Forces, and SWOT. Make sure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART).
Identify the business problems you’re aiming to solve with data analytics. For this, you can conduct an IT infrastructure audit, talk to employees, run customer surveys, benchmark your performance indicators against those of your competitors, and, should the need arise, hire external technology consultants. The experts could help you assess the digital maturity and agility of your organization, giving you realistic data analytics cost and effort estimates.
Win the C-suite’s support for project execution. It is essential to show how the selected data analytics use cases will help the organization reach the SMART goals listed in step 1. Once you get the green light, you could prioritize the approved use cases using frameworks like MoSCoW, RICE, or Kano.
Get to know your data. Prior to data analytics development and implementation, we recommend that you sift through your data to identify the information that can deliver immediate insights (i.e., hot data) and the information that does not need active management (i.e., cold data). You can move cold data to a lower-cost storage, reducing your data storage and backup costs by up to 75%.
Architect your vision. Design a blueprint of your data analytics solution architecture — and validate that it is technology and infrastructure-agnostic, vendor-neutral, and built for scale.
Create an end-to-end data management strategy. Here you should pay special heed to core data governance and democratization principles. The former would cover the management, quality, and security of your corporate data, as well as user roles and access permissions. The latter would allow any employee, regardless of their background, to access data, ask questions, and produce different types of reports.
Get down to data analytics implementation. Together with your in-house data team or external developers, you need to create data components for the priority use cases, leaving an option to scale and expand innovative capabilities horizontally to support other scenarios. The ultimate result will be an MVP of your data platform, which would cover the essential use cases and contain enough features to get value from day one while optimizing data analytics cost.
Iterate your way to perfection. Following the MVP implementation, you need to collect feedback from business stakeholders, make the necessary adjustments, and scale your data analytics efforts across other use cases. That’s how you get an end-to-end data ecosystem that eradicates the silos between your teams, departments, and components of the IT infrastructure, paving the way for company-wide data analytics.
Last but not least, brace yourself for the change. Besides promoting data literacy within your organization and clearly communicating the benefits of becoming a data-driven company, you should promote the development of cross-functional teams, minimize hierarchy, and encourage decision-making at all levels of the organization.