Using analytics in your business can increase productivity, predict market trends, assess risk, and optimize potential decisions. However, data quality problems can affect the accuracy of analytics applications.
Data quality problems affect the accuracy of analytics applications
Having high quality data ensures that analytics applications perform accurately and are reliable. This improves business processes and gives an organization a competitive edge. However, data quality is still a challenge for data-driven organizations.
There are six main data quality metrics that data managers should be able to monitor regularly. These include completeness, consistency, reliability, uniqueness, validity and accuracy. Each quality metric requires different processes and techniques.
Aside from consistency and completeness, data quality also relies on the quality of the data’s source. An unreliable data source makes it impossible for an organization to make important decisions.
Inaccurate or incomplete data can also result in missed opportunities and increased costs. For instance, an incomplete customer record can prevent sales opportunities. Similarly, sending an email promotion to a poor-quality database can result in bounced messages.
Predict market trends and assess risk
Using advanced analytics to predict market trends and assess risk can give you a leg up on the competition. It is no secret that large scale data mining and machine learning is the name of the game in the finance world. The good news is that analytics can be applied to all aspects of an organization’s operations. The result is a more informed and more profitable enterprise. From sales forecasting to the shady side effects of a merger and acquisition, analytics can make your equities and other assets perform better and smarter. A smart approach to data management can help you make the right decisions, faster. The best way to do it is to implement an enterprise level data and analytics strategy from the ground up. Whether you’re an investor, a boardroom decision maker or a department head, analytics can provide you with the necessary tools and techniques to succeed.
Optimize potential decisions
Using a top-of-the-line data mining solution, the folks at Home Credit International (HCI) were able to identify the best loan pricing options and woot ya! Home Credit was able to reduce costs by nearly 50% in the process! HCI was also able to make more informed lending decisions using the resulting insights. As a result, the company was able to reap substantial savings and increase their netbook lending volume. To this day, the HCI decision-making team remains intact and the organization has been recognized as a leading mortgage lender. Home Credit’s loan pricing optimization solution has been lauded as the gold standard by industry peers. The solution is an industry first and is being used by more than a dozen lenders across the country. With an annual ROI in the high single digits, Home Credit is able to improve loan origination while ensuring that lenders receive a fair market rate for the monies lent.
Using analytics to increase productivity is becoming an increasingly important strategy for companies to maintain competitiveness. Analytics enable strategic decision-making, reduce human intervention, and improve quality and profitability. It also helps detect fraudulent activities and identify process gaps. In addition, it can position an organization to adopt best practice standards.
As the volume of data increases, companies have to identify the most effective ways to measure worker performance. Ultimately, analytics must be implemented by dedicated individuals. These people are called data analysts. They can use data to analyze trends and find cause and effect, as well as to present findings to higher-level decision makers.
Analytics can help firms detect fraudulent activities and ensure that quality is always maintained. In addition, it can help them improve business controls and auditing. In the era of the Internet of Things, firms are faced with an overwhelming amount of data.
Modern analytics are predictive, self-learning, and adaptive
Using analytics, you can make faster, more effective business decisions. You can also get a better understanding of the current business landscape. It helps you answer questions about customers, employees, and finances. It helps you identify trends, uncover patterns, and recommend new datasets for analysis. These tools are predictive, self-learning, and adaptive. They are designed to provide a single source for data, and are easy to use.
Analytics is a key component of a modern digital enterprise. It allows business users to harness information in real time and collaborate with any device. It also provides a high-definition picture of the business landscape. Using analytics, you can share a data story in minutes. It also helps you discover hidden patterns in data.
In the early days of analytics, data was managed in spreadsheets and relational databases. These databases were designed to handle structured information. They were often used to perform comparisons between statistics. But today, many data sources are unstructured, such as emails, text, and video. These sources tax the ability of these conventional relational databases.