Data Projects Have Significant Financial Costs, and Most Fail to Deliver Value. The Right Specialised Vendor - Such as datasapiens - Can Help Overcome This.
Up To 85% Of Data Projects Fail.
Experts estimate that data analytics projects fail at a rate ranging from 60% to 85%.
There are several reasons why data analytics projects fail. Some of the most common causes include:
The need for clear goals is a big problem. Before you start, you must understand what you want to achieve with your data project. If you don't know what you're trying to do, it will be challenging to measure success or failure.
Inadequate data quality: messy data can lead to inaccurate insights and impaired decision-making. It is essential to clean and prepare your data before you start analysing it.
Lack of skilled resources: data analytics can be complex and technical. You need the right people. They need the necessary skills. Without them, your project will be impossible to do.
Poor communication and collaboration. Data analytical projects involve people from different departments. It is vital to ensure everyone agrees. There must be good communication between all stakeholders.
Failure to adapt to changing business needs: Data analytics is evolving. You must adjust your project to changing business needs. It would help if you also adapted it to changing market conditions.
Data Projects Have Significant Financial Costs.
The TCO for an in-house analytical solution can vary. The cost could range from a few hundred thousand to several million dollars. The price depends on the scope and scale of the solution. Here are some of the potential costs associated with this approach:
Making a data platform from scratch takes a lot of time and resources. You may need data scientists, engineers, and software developers. These professionals have high salaries. This can add up to a significant cost.
Hiring costs for finding skilled data analysts can be high. And keeping them can be expensive for companies. This is especially true for those not known for being tech leaders. This can make building and maintaining a robust data analytics team hard. It hinders the platform's effectiveness.
You must invest in infrastructure to support the data platform. Investment in servers, storage, and networking if opting for on-premises solutions. Cloud-based solutions might reduce initial hardware costs but will have ongoing operational expenses.
Maintenance costs are a vital concern. The platform needs ongoing support to stay stable and fast.
Data Integration Costs: Companies often have data spread across many systems. These include point-of-sale (POS) systems. They also include inventory management and customer relationship (CRM) systems. Integrating this data into one platform takes time and effort. It leads to more costs.
Opportunity costs are at play. Making a data analytics platform in-house can take time and resources. It takes them away from other critical business priorities. This can delay other initiatives. They could have a more immediate impact on the company's bottom line.
Example Estimation Approach
Initial Development: $500,000 to $5,000,000, depending on the scale and complexity.
Annual Operational Costs are 15-20% of the initial development cost. This includes staffing, maintenance, cloud services, etc.
Indirect Costs are variable and hard to predict. But risk management strategies should factor them in.
Specialised Vendors - Such as datasapiens - To The Rescue
Hiring specialised vendors for data projects can cut the risks. These vendors bring expertise, experience, and proven methods. Here are several ways in which engaging these vendors can help:
Expertise and Specialisation
Access to Expert Knowledge: Data analytics vendors often have deep expertise. They know about machine learning, big data, and business intelligence. They specialise in these areas. This expertise can be invaluable in ensuring project success.
Specialist vendors have Industry Experience. They usually have experience across different industries. This allows them to bring best practices and new insights to your organisation.
Risk Reduction
Vendors have tried and tested methods that reduce the risk of project failure.
Vendors can scale resources up or down as needed. This is more efficient than hiring full-time staff.
Cost Management
Outsourcing to a vendor can make costs predictable. It can convert variable costs (like hiring and training an in-house team) into a fixed fee.
Vendors often have economies of scale. They have things like bulk licenses and shared resources. These things can reduce the project's cost.
Access to Advanced Technologies
The latest tools and technologies are often cutting-edge. Vendors have access to them. They might be too expensive or complex for a single company to get and maintain.
Vendors will likely update their tools and methods often. They do this to stay competitive. This ensures your project benefits from the latest advances.
Focus on Core Business
It reduces strain on internal resources. Outsourcing analytics projects lets your team focus on core activities.
They can often complete projects faster due to their expertise. This leads to quicker benefit realisation.
Customisation and Integration
Vendors can offer tailored solutions. These solutions align with your business's needs and goals.
They have experience in adding new solutions to existing IT. This is critical for seamless operation.
Conclusion
You need to pick a vendor with the right skills to cut the risks and costs of data projects. They should also fit with your business culture and understand your industry's challenges
Ready to streamline your data projects and maximize value? Contact us today to discover how datasapiens can transform your data analytics journey.
