It is evident that 99% of businesses, irrespective of big or small, is bleeding heavily due to data silos. The current environment demands huge dollars to be spent in order to maintain data, which covers paying for redundant data and isolated data due to data sources that don’t provide a common unique parameter for unification. Data grows on a daily basis and there is no escape from the cost incurred on growing redundancies and data silos. This sub-optimal approach not only costs a lot of money but also create gaps in achieving the desired results.
Problems associated with data silos can be many. For example, if you are facing the problem of higher customer acquisition costs, you can see that this is due to (a) inaccurate attribution models leading to ineffective ad spends and (b) lack of customer engagement or irrelevant targeting. Both these problems are resultants of data available across many data sources and lack of unique tags to merge them. So, the analysis carried out over individual data-sets results in wrong analysis, which results in low effectiveness and wasted resources. Continue reading →