Data Analytics Cost & What Affects It
Data analytics adoption rates have reached a historic high of 25%across companies of all sizes. According to MicroStrategy’s 2020 BI & Analytics survey, 63% of businesses procure data analytics tools to become more efficient, while 51% of enterprises that tap into data analytics seek to improve their financial performance.
But it’s not all rainbows and unicorns for data-driven companies.
According to another survey conducted by 1PATH, 35% of the respondents cite extended implementation cycles as the key barrier to data analytics implementation. In addition, 42% and 27% of companies express concerns about the technology’s complexity and high cost, respectively.
Amid the global recession, the cost factor remains as acute as ever. That’s why we decided to explore it in greater detail.
How Much Does Data Analytics Cost?
As much as we’d love to give you a definitive answer, there are just too many factors affecting the cost of data analytics in enterprises that one needs to consider.
These include a company’s data analytics objectives, the amount and quality of its data, the chosen data analytics technology stack, and overall digital maturity and organizational agility.
Let us discuss these factors one by one.
Factor #1: Nature, Quality, and Amount of Corporate Data
Between 2010 and 2020, the amount of operational data generated by enterprises increased by 5,000%.
Today, companies that use Big Data analytics solutionsanalyze information from 400 sources on average. These sources might include IoT systems, CRM software, and social networking platforms, to name a few. And up to 90% of this information is unstructuredand better suited for AI/ML-driven analytics.
Therefore companies that want to turn this well of data into actionable insights must first look for Big Data consulting services. Then, together with skilled business analysts, cloud computing engineers, and software architectures, they must assess their current IT infrastructure, identify the key data sources, and devise an effective data management framework.
Unfortunately, data experts do not come cheap. This report by Accelerance, for example, indicates that US data specialists’ hourly rates may range from $82 to $170.
Factor #2: A Company’s Data Analytics Goals
There are four types of data analytics your company could aim for:
- Descriptive analytics involves tapping into historical data to determine what went wrong and when and why it happened. Such information is typically used for canned and ad-hoc reports, such as the ones prepared by your PPC or SMM specialist monthly and upon campaign completion.
- Diagnostic analytics matches historical data against information from other sources to determine what happened and what your company can do about it. If your sales department closed few deals last month, you might leverage diagnostic analytics to find the culprit.
- Predictive analytics helps detect correlations between past and present events and anticipate trends. This information may be useful when planning sales volumes based on customer demographics and seasonal factors.
- Prescriptive analytics uses Big Data and AI/ML algorithms to spot patterns in information, identify the cause of certain events, and recommend steps to mitigate risks, acting as an advisor to your employees.
Factor #3: Your Data Analytics Toolset
Depending on your analytics objectives, you might set your eyes on the following types of data solutions:
- Standalone tools range from SaaS products like Power BI and Tableau to advanced open-source solutions like RabbitMQ. These data tools solve narrowly defined business tasks — for instance, synchronizing data sources, locating your inventory, or visualizing miscellaneous operational data. However useful, standalone solutions are poorly suited for achieving company-wide data analytics.
- Industrial data platforms offer a single place to manage your company’s operational data. Products like Kissflow, Salesforce, and SAP represent this category. Compared to standalone tools and utilities, industrial data solutions can help you meet most of your data analytics objectives. And platforms like Salesforce even feature pre-built AI tools that feed off your operational data to make smart predictions. The only drawback we could think of is the high price of such tools, which might become a serious issue as your company grows and attempts to scale data solutions across multiple use cases and departments. For instance, using SAP could cost your company at least $14,000 annually. Industrial platforms may also require extensive customization to meet your specific needs, which could total $10,000-100,000 for a Salesforce-based data analytics tool.
- Integrated data ecosystems combine open-source, SaaS, and custom-made data analytics tools and fully address your analytics needs. Their main advantage is the ability to source information across your company’s IT infrastructure and external resources. Should the need arise, it’s also possible to enhance them with out-of-the-box or bespoke AI/ML models and thus tap into predictive and prescriptive analytics. To build a scalable and effective integrated data ecosystem, avail yourself of modern data architecture consulting services and carefully weigh your options.
Summing up this section, standalone, industrial, and integrated data solutions are comparable in performance and functionality; it’s just that they serve different purposes.
The wrong choice of a data solution may result in poor analytics, unforeseen customization expenses, and low employee satisfaction rates.
Returning to the 1PATH survey we referenced earlier, 49% and 35% of businesses believe their data analytics tools are too difficult to use and do not function as expected. Given that nearly half of the respondents purchase data analytics tools for $10,000-25,000 and pay an equal amount in customization and maintenance costs annually, skipping the audit and planning part may be too costly a mistake to make.
Factor 4: A Company’s Agility and Readiness to Change
The “organizational agility” term defines the ability of a company to analyze, respond, and adapt to the evolving needs of its market and target audience.
What does it have to do with the cost of data analytics?
Recent studies show that up to 65% of companies have experienced resistance on employees’ part when implementing data analytics solutions, despite initially assuming no such difficulties would arise.
This scenario is common for companies that fail to clearly communicate their strategic goals and the benefits of adopting analytics tools to their workforce.
A lack of employee training can be a problem, too. For example, if your supply chain managers have been using Excel for a quarter of a century, they might have difficulty mastering Tableau or SAP without expert guidance. This may extend your data analytics implementation cycle and delay technology investment returns.
On a Final Note
Here are some tips you should follow to become a data-driven company while optimizing data analytics costs:
- Formulate your company’s digital transformation mission and goals, backing up your assumptions with PESTEL, VRIO, SWOT, and other analyses.
- Identify the problems you’re aiming to solve with the help of data analytics and tie your objectives to KPIs.
- Get the support of the C-Suite to secure a budget and execute the project.
- Partner with reliable data warehouse consultants to determine what data you have, in what state it is, and whether it can produce insights immediately.
- Create a blueprint architecture of your data solution and a comprehensive data management strategy with your technology partner.
- Start implementing the data analytics solution step by step, focusing on smaller tasks while having a bigger picture in the back of your mind.
- Having implemented the data solution, collect user feedback, adjust the system’s performance, and gradually scale it across other processes.