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3 Approaches for Making Sense of Big Data in Retail

There is a demand like never before for companies to become increasingly strategic in how they manage and analyze big data. The problems companies are solving today have a heightened increase in complexity. Companies have more data than they have ever had before, and this is creating a growing demand for methodologies and tools that can sift through, filter and analyze big data so that the relevant information is communicated to the key sources. The key lies in learning to combine web analytics data with social and search signals. The Problem Big data refers to an enormous amount of data that is too large to process using the previous models available. Often the data is not just too big, but also moves and changes too fast for current capacity to process it. Data Integration The first step in tackling and processing big data is to harness the incoming data in a meaningful way. Data has to be gathered together from multiple inputs into a single place in a way that layers and organizes the data into separate comprehensible streams. Data is gathered from different channels and networks and brought together into a format that is comparable. The key lies in utilizing analytics platforms that allow data to be gathered up from different input channels and then sorted out, processed and then disseminated in a way that makes it actionable for the company. Select Relevant Data The importance in knowing which data is the most relevant to focus on is critical for making sense of big data. For this reason there needs to be a set of metrics by which to measure and gauge performance. By combining web analytics data with search and social channels, you can then connect that to business value. An example of this would be found in looking at the connection between conversions and search engine rankings. After determining which data sources are important for your marketing strategy, you must then ensure that you are gathering data from trusted sources. Common sources for critical data gathering can include keyword data, traffic data, local search and social network data, to name a few. Filter Irrelevant Data Setting boundaries for incoming data within a given organized framework is extremely empowering as it frees up energy to focus more heavily on that which is producing the largest effect. Determine your key performance indicators, or KPIs. These will help you form a marketing strategy that drives the data channels you use. To keep from being completely bombarded by data, it’s essential that you set these guidelines so that data gathered works for you, rather than overwhelming you. For more information about Mobile Insight visit our website :

3 approaches for making sense of big data in retail