The success of your outsourced analytics project depends as much on ‘how’ you set up the working relationship, as it does the quality or skills of the vendor. Watch out for these common hurdles on your path to success.
In 2019, the Global Data Analytics Outsourcing Market was valued at $3.04 billion, and it is expected to reach $9.46 billion by 2025 at CAGR of 21.5% (2020-2025). As the volume and variety of data being generated rises, organizations find it challenging to manage and analyze massive amounts of data systematically. That’s why they turn to analytics vendors whose technical expertise and best practices they can leverage.
However, outsourcing analytics is only the beginning of the journey. The end outcome that clients desire from such outsourcing are insights that fuel the business growth, help reduce operational costs or delight their
customers. Thus, finding a credible data analytics vendor becomes extremely important.
Working with the right analytics vendors can help you meet your business goals in a cost-effective manner. AnalyticsGenie dramatically simplifies your shortlisting of analytics vendors from around the world at www.analyticsgenie.com. Here, you can read more about the expertise and the capabilities of the vendors listed on the site. You can also learn more about the projects handled by the vendors when you visit their website.
Across such projects, there are some mistakes one commonly comes across, which hamper these outcomes. Here is how to avoid them.
1. Identify the correct business problem and align with the stakeholders – It is critical that all stakeholders agree to the business problem and align to the objective of undertaking a data analytics
exercise. A wrong problem statement or mismatch in the objectives of stakeholders will render the exercise ineffective or futile. Sharing their experience with data analytics outsourcing, Rajat Gandhi, Founder & CEO, Faircent.com says, “We were facing issues of objectives alignment while outsourcing our analytics needs. The requirement was unclear as they came from different departments having different ideas.”
How to address it – Ganes Kesari, co-founder of Gramener, heading Data Science Advisory and Innovation says, “Never start data analytics initiatives by talking about data or analytics. Brainstorm to identify all the roadblocks for your business objectives. Prioritize them based on three factors: business impact, urgency, and feasibility. Pick the top-ranking business problems from this list and carve out a solution using data science.”
2. Define the end-use objective of data analytics – It is important to define the objective of the data analytics exercise. This could be diagnostic or predictive and corresponding analytical tools would
need to be applied. Confusing the objective of the analysis and applying the findings of a diagnostic analysis to predict behavior or vice versa could lead to misleading results. In some cases, business teams may already have a conclusion in mind and try using data to confirm that hypothesis while disregarding other relevant pieces of information.
How to address it – The goal of specific, time-defined or ongoing analytics exercises should be aimed at finding the optimum solutions. This should be clearly articulated along with the problem statement and shared with your analytics vendor to avoid misinterpretation. Data analysis should proceed without any bias or conclusion as a starting point.
3. Keep assessing the data strategy and watch out for changes in external factors – It is essential to scale up or realign data management projects in a dynamic business environment, ensuring that your data
strategy is aligned to your business strategy. With rapid changes in the external environment, continuing with obsolete data management practices, whether in terms of capture, analytics, or data ownership within the organisation, is one of the common reasons projects don’t deliver,
How to address it – Have a communication mechanism in place, in order to periodically answer key questions together with your analytics vendor, such as:
- What new business goals does our data strategy need to be aligned to?
- How is the ecosystem in which we deploy our insights changing?
- How are our data sources evolving, and what should this mean for how we manage data?
- Are internal users accessing the data/insights that they need to deliver on their KPIs?
Set aside budgets, resources and expectations for periodic revisions, based on the answers to questions like these.
4. Ensure good quality data and protect data integrity – Data is abundantly available, but often, data capture is from a variety of sources, and in different ways.
Challenges may arise from practices such as manual data entry co-existing with automation enabled data or unstructured data being captured in different ways by different geographies. Most companies are still evolving a robust data governance process. Consequently, validating data integrity becomes challenging.
How to address it – It’s vital for companies to manage data in a clean environment and ensure that high quality and credible data is readily available for analysis. While legacy issues may mean this cannot be solved immediately, taking steps to put in place a data quality management program will help. Educating internal users on the need to collect high quality data, rationalizing unstructured data, digitizing collection processes to the maximum extent possible and including your analytics vendor in the process will all help to improve project outcomes.
5. Stay involved to monitor progress – Once a company outsources the data analytics work, it should not assume a hands-off attitude.This may result in delays or quality getting compromised due to lack of
involvement and feedback. How to address it – While involvement is higher at the discovery stage and should reduce over time, it is still prudent for the company to be connected regularly to the data analytics vendor for timely feedback and monitoring to ensure things are on track and push for remedial action if warranted.
In general, data analytics requires patience. One size fits all answers may not be available. A business needs to be clear about what its goals are from a data analytics exercise and the quality of data available for this analysis.
According to a Forbes study in 2019, more than 150 zettabytes (150 trillion gigabytes) of data will need analysis by 2025. Harnessing the power of this exponentially growing data can be a smoother journey if companies keep the above suggestions in mind.
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