Like any raw resource, data tends to go through many transformations before it reaches the market. Unlike physical resources, though, dirty data is the same as bad data. With 40% of business initiatives failing due to bad data, the consequences are real. Although many things can go wrong along the path from collection to reporting, there are four practices that must be put in place to assure that data specialists are starting with useful information. The following four practices align with the four stages of bringing data from its raw source to the point of data staging just prior to analysis.
All across the world, companies are utilizing predictive analytics that were never available before the public cloud became so affordable. All areas of business where critical decisions once depended only on intuition and educated guesses are being improved immensely by predictive analytics, machine learning and business intelligence. Here are the top ways they are being deployed now.
Many businesses are still adjusting to the reality of big data, but the same pressures apply, to some extent, for everyone from the front line to executive leadership. The following is a guide to prioritizing actions around big data projects that involve sources such as online metrics, automated processes, mobile devices, IoT sensors, conversations with customers and social monitoring tools.
Most companies are just now learning how to operate data-driven projects effectively. Flawless execution remains elusive for many data project managers, due to the 6 most common data roadblocks. The following analysis of project roadblocks and suggested ways through them could end up saving you substantial amounts of time and resource commitments.
Companies using analytics to make verifiable predictions about customer behavior have two pathways for earning hard returns on that data. They can market their insights directly to business partners or they can build out their own new lines of business based on their analytical projections. Here are a few examples of how that works.
Technology enables businesses to capture customer data points faster and in greater quantities than ever before. Not only does this mean that personalization at scale is possible, but that it is quickly becoming a consumer expectation and a business necessity. Learn how companies like Amazon, Delta, and 23andMe are using big data to effectively support their business model and solve real problems for their customers.
If knowledge is power, data should have enough power to drive an entire enterprise. It can if it is applied in the right way. Many companies are still trying to get a grip on the practical uses for data. Take a look at seven of the ways that market leading businesses are deploying data in effective ways to drive revenue and cut down on expenses.
Traditionally, customer service has been treated as a necessary expense, but now the fight for customer loyalty is being waged with data-driven insights. Companies winning at customer service, like Amazon and Netflix, understand that using big data strategically has positive effects on repeat business, overall cart sizes and referrals. In short, the way back to the kind of quality human interactions that used to be common at local merchants is only through better data analytics.
Intelligent website form design may be the most crucial tool in your online arsenal. This step represents the reflection point where people decide whether to engage more fully or bounce away. If the value hidden behind the form is perceived to be lower than the amount of irritation generated by filling out the form, the relationship is over before it began. Fortunately, recent technological advances in the development of APIs have made online forms smarter by an order of magnitude.