Business leaders require timely, accurate insights about company performance to make the best decisions for growth, products, and client retention. Dhiren Patel, chief product officer & head of customer success at MachEye, says, with the exponential increase in data on a daily basis, it’s important to review one’s data analytics approach to deliver critical insights for success.
Comprehensive Data Management Strategies Are Essential
Many companies lack a formal strategy to collect and store data for easy access. In fact, some companies still use manual processes and basic reporting to leverage data stored in siloed legacy databases. As a result, they are unable to fully leverage this data in their data analytics solutions to apply to daily business decision-making. Common data management challenges include:
- Lack of data collection
- Poor quality of available data
- Different versions and currency of available data
- The right data that answers critical questions
- Large amounts of raw data without an automated way to combine and analyze accurately and quickly
Once these data management issues are resolved, you’re ready to maximize their value using advanced data analytics solutions.Â
Accelerate Data Analytics Success: Top 5 Challenges and Tips
Data analytics has emerged as a core component of corporate digital transformation efforts. The exponential growth of data is overwhelming the capacity of legacy data management systems that are burdened with siloed databases. It is nearly impossible to mine this data in a meaningful way to generate trusted business insights.
We have identified five data analytics challenges to keep in mind as you explore new solutions for your data analytics ecosystem.
1. A User-Friendly Experience
Traditionally, analytics solutions have been very complicated to use. They require data scientists and/or business intelligence specialists to run requests received by business users throughout the company. As expected, requests are backlogged, although speed is most critical. This is one reason why decision-makers continue to use Microsoft Excel as their fallback tool for making sense of business data.
Tip: It’s important to use a data analytics solution that is easy to use by non-technical business users so they can make faster decisions.
Learn More: Data Warehouses: Why a Single Source of Truth Is Necessary for Customer Analysis
2. The Cloud
Companies are rapidly shifting their business solutions to the cloud for speed, 24/7 accessibility and cost savings. Having a cloud strategy for a data analytics solution is essential. The solution should leverage the advantages above, in addition to providing these three features: a single source of business data for consistency, infinite scaling for data growth, and easy, secure access by business users. In contrast, some legacy solutions use offline data to create static dashboards and reports. This outdated approach raises concerns about data currency, accuracy and quality.
Tip: Cloud provides the required scalability and quality for your analytics output.
3. Change Management
A big problem with current data analytics solutions is user complexity. Any change management expert can tell you that employees will not adopt complicated solutions, as opposed to more intuitive solutions that empower them to do their jobs faster and easier. Continued investment in complex legacy solutions only exacerbates this adoption problem.
Tip: More investment in overly complex legacy data analytics systems should be weighed against the benefits of modern, flexible solutions that business users can easily use to pull and analyze data − independently.
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4. Advanced Insights
AI and machine learning continue to make data analytics software significantly smarter. Review your current data analytics solutions to assess if you are getting basic high-level reports or deeper learning that reveals significant core business insights. Advanced insights are immediately actionable thanks to unrivalled detail and granularity. You can confidently refer to them to support changes in products, business direction, recruitment, and internal processes.
Tip: Consider a single solution that offers advanced insights to support actionable changes, not just basic reports and dashboards.
5. Presentation of Data
The presentation style of data analysis can make or break the speed of decision-making. They can range, for example, from a dump of unlabeled data to many confusing charts. Reports often need to be translated by a technical person for the business user. This extra step in the process simply wastes time. Instead, the initial report should provide easy-to-consume insights that can be quickly studied and put into action.
Tip: It’s important to simplify data analytics results with consumable narrations and stories that focus on actionable key findings.
Learn More: Search Engines: The Only Hack You Need To Reorganize Your Data in 2021
Ready To Transform Your Data Analytics?Â
Advanced data analytics are shifting to a modern cloud-native paradigm that offers scalability and flexibility. New-age business intelligence and analytics solutions offer natural language conversational interfaces to interact with data plus advanced insights in the form of stories and audio-visuals. The global enterprises of today are in a position to create the DNA of analytical decision-making by leveraging modern, cloud-native, user-friendly solutions that also bring the power of AI and machine learning to everyone. Consuming advanced analytical insights in an enterprise can be fun and easy.
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