Top 5 Reasons Why Data Strategies Fail
Organizations are increasingly faced with challenges of providing reliable data for consumption to fuel better business decisions. As a result, many organizations are developing a Data Strategy to try to overcome data silos and to put data to work in their business to augment critical decision-making processes. However, according to Gartner, in 2016, 60 percent of data initiatives would fail and by the end of 2017, this number was much closer to 85 percent. Identifying the reasons for failure can help you avoid this outcome. Here are the top 5 reasons why data strategies fail:
1. Weak Analytics Strategy
Put simply, the importance of data and your analytics strategy is often underestimated by the business. Although the trend is towards understanding the value of data, we are not in a place where leaders have invested in a more data-centred approach, rather than just seeing data as a by-product of their business activities. Building a solid Data and Analytics Strategy will drive alignment in how you acquire, store, manage, share and use data for business decision making and to drive more strategic goals for your business. A strong Data Strategy ensures business alignment, clearly defined goals, detailed data requirements and solid KPIs to showcase value.
2. Low Commitment to Data Governance
Despite the importance of Data Governance, only 41% of all companies have a defined data governance program in place and 20% of companies have senior leadership that fails to see the value. Data Governance is the process of getting the RIGHT information to the RIGHT people at the RIGHT time. It encapsulates security, responsibility, data trust as well as the required regulatory compliance. Data Strategy and Data Governance go hand in hand.
3. Poor Execution
Data analytics is not about reporting on your data it is about innovating with your data to achieve new goals, increase efficiencies, reduce costs or even drive new business opportunities. New financial reports for example are important for your business, but they do nothing to predict future financial opportunities. The best way to execute your Data Strategy is to take a top-down approach starting with your business priorities and then a bottom-up approach with your data starting with your domain knowledge and your data sources and then find relevant data sets to answer your hypotheses. Next, build a flexible architecture with clearly defined integrations and workflows. Identify the need for new ETL tools, a Data Lake, Machine Learning tools. Finally, track your ROIs to ensure your execution matches your goals.
4. Shiny “New Toy” Syndrome
When building out a data analytics strategy, most businesses usually create a roadmap that resembles this:
- Collect the data from different sources into a Data Warehouse
- Funnel the data into a business intelligence tool
- Take advantage of insights
This sounds easy, however, you often end up confusing analytics for off-the-shelf software. Developers get excited with the new shiny tools and build reports however they often end up frustrating the end-users when these reports don’t deliver the insights they were looking for. The best approach is to start simple and then innovate and scale to meet the real business demand. Start with your historical data to summarize the results of your business and then start to make inferences with simple algorithms followed by time-sensitive decisions to drive real value.
5. The Old School Mindset
If we are going to use new tools to solve old business problems, we also need to use a new mindset. Now is the time to incorporate agility into your IT organization. Ensure that your organization can adapt to the change and going complexity. Once we shift our mindset, our ability to drive real business value will be in hand.
Drive your Analytics Strategy with clear business goals, organizational support, proper governance and agility.
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