Analytics projects are expensive and difficult. There are two fundamental requirements for undertaking data analytics projects: clean, integrated, meaningful data, and skilled resources in the form of specialist data scientists.
In recent years, we have seen industries take huge leaps of faith, pouring significant investment into analytical projects with mixed results. Trying to make sense of bad quality data that is disjointed, siloed and filled with redundant and obsolete information can, at best, create huge delays in getting meaningful results or, at worst, drive wrong decisions through misleading correlations and trends.
In most instances, organisations don’t know what value they will get from analytics until the analysis is actually done. This makes it extremely difficult to construct a business case for investing in analytics projects in advance.
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