Top 5 Factors Behind Data Analytics Costs

A would cost at least to build and implement. Most companies that opt for SaaS-based data analytics products end up paying per year in vendor and maintenance fees custom integrated data analytics solution $150,000-200,000 $10,000-25,000 The cost of data analytics depends on multiple factors, including the amount and quality of your corporate data, your data analytics needs, the choice of data analytics tools, customization efforts, and organizational agility. To reduce data analytics cost and get a faster payback, your organization needs to define clear data objectives, develop a data management strategy, select primary use cases, and gradually scale your analytics efforts while gathering stakeholder feedback. Data is the fuel that keeps the wheels of your business turning. Most companies only realized that during the pandemic, so data analytics spending soared in the past two years. A recent study by Ernst & Young revealed that 93% of CEOs for the time being. consider data and analytics their top IT investment priority What goals do businesses pursue when tapping into or procuring off-the-shelf analytics tools, anyway? custom Big Data application development According to the BI & Analytics survey conducted by MicroStrategy in 2020, 63% of businesses splurge on data analytics tools to become more efficient, while 51% of the respondents seek to improve their financial performance. 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 , so let’s talk about data analytics cost today. boil Big Data down to meaningful insights 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 Organizational agility and willingness to change Factor 1: Amount, nature, and quality of your data Back in 2016, the average company ; in enterprises, the data volumes typically exceeded 347TB. was managing 162.9TB of data Six years on, companies 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. see their data grow by 63-100% Analysts estimate that 80-90% of business data is . This data comprises images, videos, audio files, uneditable documents, and sensor readings, so you cannot store it in traditional relational databases. unstructured 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 . 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. data lakes and data lakehouses That’s why the cost of data analytics includes the expenses associated with devising an end–to-end , setting up a complete data storage infrastructure for company-wide data aggregation, and bringing unstructured data to a unified format. Overall, these expenses . data management strategy 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: . 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 . 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. Descriptive data analytics ad hoc or canned reports . 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: Diagnostic data analytics The first approach is called . 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. query and drill downs 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. Discovery and alerts . 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: Predictive data analytics helps find dependencies between different parameters — say, geography and revenue. Statistical modeling With this data, it is possible to further calculate sales for various target audiences in a particular region — and that’s where comes in useful. predictive modeling . Powered by Big Data and , 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 . Prescriptive data analytics artificial intelligence algorithms optimization and random testing techniques Subsequently, we need different tools to perform those types of analyses — and data analytics cost will increase proportionally with the platforms’ feature set, complexity, and integration capabilities. According to , ITRex Group CEO and Co-Founder, we could segment modern data solutions into three categories: Vitali Likhadzed 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., , 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. Standalone tools and utilities. visualizing operational data 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. Industrial SaaS platforms 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 to spot recurring patterns in both structured and unstructured data, anticipate events and scenarios that could affect your business, and automate tedious tasks. Custom data systems are vendor-agnostic, extendible, and tailored to your processes, business functions, and usage scenarios. This helps unlock new opportunities and revenue streams, thus maximizing return on your data analytics investments. Integrated enterprise-wide data ecosystems artificial intelligence capabilities 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: Data analytics solution Feature set Pricing Microsoft Power BI ● Data aggregation across your IT infrastructure ● Structured and unstructured data analysis ● Rich data visualization options ● Advanced reporting ● Convenient search powered by natural language processing (NLP) algorithms The tool’s functionality varies depending on the actual plan: ● Microsoft 365 E5 users can purchase Power BI for**$9.99/month $20/month $4,995/month**, your company could get unlimited seats, all the features available in the $20 Power BI Premium plan plus the option to auto-scale your data analytics efforts with your current Azure subscription per seat, getting access to self-service analytics and customizable dashboards ● The per user plan covers AI-driven Big Data analytics ● For Tableau ● Data flow creation and editing ● User roles and permissions management ● Collaborative dashboards ● Data-driven alerts and reports ● Smart predictions and recommendations with Einstein Discovery In Tableau, data analytics cost would again depend on the functionality of the chosen plan: ● The**$15/month* $42/month* $70/month***per user, is best suited for analysts and senior managers who define data workflows and create dashboards for others to consume per user plan enables users to view and interact with dashboards ● The Tableau Explorer mode unlocks self-service BI capabilities, empowering users to interact with data and seek answers to their business questions ● The Tableau Creator license, which costs *billed annually TIBCO Spotfire ● At-scale predictive, geolocation, and streaming analytics ● Interactive, multi-layer data visualization dashboards ● Rapid application development and deployment with Spotfire Mods ● Built-in recommendation engine uncovering insights in your data ● Integrated tools for data wrangling — i.e., preparing the information aggregated from multiple sources for analytics The TIBCO Spotfire data analytics pricing depends on the product you’ll choose: ● The TIBCO Cloud Spotfire platform comes with a 30-day free trial; the basic package includes two analyst, two business author, and two consumer seats. Following the free trial period, you’ll be charged**$125/month* $0.99/hour**. ● The data analytics pricing information for the TIBCO Spotfire Platform and TIBCO Cloud Spotfire Enterprise is available on demand. , , for every seat and user role, respectively. There’s a fee for 25 GF of storage. ● With the Spotfire for Amazon Web Services, you’ll get the data analytics service available as an AWS instance. Its pricing starts from $65/month $25/month $25/month *billed monthly or annually SAP BusinessObjects Business Intelligence Suite ● User-friendly BI tools for data analysis, reporting, and ad hoc queries ● Self-service analytics supported with customizable graphical reporting formats ● Effortless collaboration and content sharing across the enterprise ● Scalability driven by the mighty SAP Cloud Platform ● Possibility to leverage machine learning for predictive analytics 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. Salesforce Following Tableau’s acquisition in 2019, Salesforce provides two types of data analytics solutions — sales analytics with the and the data analytics tools offered by . The former comes with robust functionality for sales and marketing analytics: ● Dashboard design and customization ● Intelligent forecasts with Einstein Prediction Builder ● Data preparation and orchestration ● Revenue insights ● Multiple add-ons to extend the functionality of the Salesforce platform Salesforce CRM Tableau If you’re considering using Salesforce CRM Analytics, there are four products to choose from: ● Einstein Predictions for automated discovery and forecasts, which costs**$75/month* $125/month* $150/month* $200/month***per user per user ● CRM Analytics Growth for end-to-end management and analysis of your data, which is available for per user ● The CRM Analytics Plus platform with AI capabilities, which costs per user ● Revenue Intelligence for purpose-built and AI-driven analytics, which costs *billed annually 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 that’s not to mention software configuration and employee training expenses. According to a , 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. survey conducted by 1PATH 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 to tweak the tool to best suit your unique business needs. could easily spend $10,000-100,000 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 ITRex Group partnered with a retail juggernaut operating over 10,000 grocery stores, discounters, and hypermarkets in 20+ countries. The customer wanted to create an providing on-demand, hassle-free access to critical information for three million employees scattered across the globe. AI-based self-service BI platform Following a , we outlined the project scope: discovery phase 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. to empower users, regardless of their technology background, to interpret data insights and create ad hoc reports Build a self-service BI platform 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 — 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. $150,000-200,000 Factor 5: Organizational 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 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%). 65% of organizations have experienced employee resistance 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 in just three years. will become unbridgeable The good news is, you can reduce the cost of implementing data analytics and reap its benefits faster by following the steps below: . 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). Define the mission and goals of your organization with regard to digital transformation . 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, . The experts could help you assess the digital maturity and agility of your organization, giving you realistic data analytics cost and effort estimates. Identify the business problems you’re aiming to solve with data analytics hire external technology consultants . 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. Win the C-suite’s support for project execution . 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, Get to know your data reducing your data storage and backup costs by up to 75%. . Design a blueprint of your data analytics solution architecture — and validate that it is technology and infrastructure-agnostic, vendor-neutral, and built for scale. Architect your vision . 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. Create an end-to-end data management strategy . 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 costs. Get down to data analytics implementation . 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. Iterate your way to perfection 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. And should you need help customizing a standalone or platform-based data analytics product (or developing a custom data ecosystem from scratch!), we’re happy to accompany you on your transformation journey! Just to discuss your data analytics needs. drop ITRex team a line