Businesses are abuzz with the promise of advanced analytics and its effectiveness today. Tied to a business strategy, a well-planned analytics initiative can make a big difference to an organization. The key drivers for business analytics are extremely compelling and it is critical for strategic decision-making. It can accurately provide financial and operational forecasts, optimize business operations and streamline processes, and has the ability to predict new opportunities.
According to an analysis published by Harvard Business Review, companies that relied on data-driven decision-making were on an average, 5% more productive and 6% more profitable than their competitors.
A TDWI Best Practices Survey Report suggested that more than 50% of respondents felt that next-generation analytics was extremely important to strategic decision-making, with just fewer than 50% saying it was extremely important to improve business processes and performance. In fact, advanced analytics is poised to double in use over the next three years.
However, the next generation of analytics are now being defined by factors which are fairly new and results driven. The reality is that answers take far too long to come or far too much cost, and also lack consistency. The ability to discover and access the right data at the right quality in the right timeframe is frequently a bottleneck. Often, the data sets are by themselves inaccurate or incomplete, and, as a result, confidence in the analysis is low. With the changing scenario, even after significant investments in next-generation analytic tools, teams, software, and warehouses, a large proportion of analytics projects usually underwhelm, disappoint, or outright fail. So, if next-generation analytics are not living up to expectations – and the applications and the people working on them are not the problem – then what is?
Data quality problems and data integration challenges are the two most common barriers to analytics success. If the data going into the analytics stack is dirty, noisy, duplicated, incomplete, poorly integrated, or delivered too late, the insights coming out of it cannot be trusted. Great data should be accurate, de-duplicated and complete. It is also easily scalable and avoids reinventing the wheel over and over again.
With the rapidly changing technology for analytics data storage, it is rewarding to build a data management architecture that works across any type of data and any storage technology. So an effective data governance strategy is a sure way to oversee data quality, manage security and compliance, or standardize processes. Concerted efforts should go into automating processes so that decision makers can get the insights they need in time to take action.
Fundamental to this process is the need to define a data management architecture for all of your analytics projects and initiatives. In this way, you will be able to drive standardization, automation, and productivity of data delivery in support of your business initiatives. This architecture needs to work across any data source, any analytics use case, any analytics tool. It is also important to make data management work across new, unstructured data types such as Big Data. Organizations are increasingly finding that the most interesting and useful business insights come from combining data from internal sources with data from external, less structured sources.
At the same time, by focusing only on IT, organizations can be at risk of thinking purely in technology terms, when they should be concentrating on the business outcomes to be delivered. It is important to bring the business team into the process of functionally defining the new data management architecture and to ensure that business analysts are getting the data they need with the quality and speed they require in order to be able to support these business initiatives.
The value of analytics solutions does not just lie in the ability to deduce insights from large amounts of data. It lies in their ability to improve critical business outcomes. The ability to deliver great data, coupled with smart data management, is the key to delivering next-generation analytics success.
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