The intersection of statistically based insight and the realization that information can be an asset has had and will continue to have serious reverberations in the business world. Being smarter has always meant being successful; as far back as the 19th century, analytics was already generating competitive advantage.<\/p>\n\n\n\n
Success requires more than just knowledge of statistics or ways of dealing with \u201cbig data.\u201d Execution is essential, but without a plan and commitment, little happens. Success also requires an understanding of how analytics translates to competitive advantage.<\/p>\n\n\n\n
Possibly the best-guarded secret in business analytics is that, in practice, its success comes down not only to organizational culture but also to the ability of managers to successfully sell the value of analytics. As researchers such as Thomas Davenport and Jeanne Harris have rightly pointed out, overall success can often be linked to a variety of factors including organizational structure, management commitment and successful strategic planning. However, it\u2019s often \u201cwhere the rubber hits the road\u201d that the greatest impact can occur.<\/p>\n\n\n\n
Analytics is a multi-disciplinary activity: the value from insight comes not from the activity but from the execution. Often, this crosses a variety of departments within an organization \u2013 few analytics groups have responsibility for both the insight creation and the execution of that insight. Because of this, selling the value of analytics isn\u2019t just a goal for managers; it\u2019s a necessary criterion for success.<\/p>\n\n\n\n
For many managers, this can be challenging. Despite broad interest in business analytics as a discipline, most organizations have a relatively immature understanding of what \u201cbusiness analytics\u201d is, let alone how it creates value. When projects fail, it\u2019s all too easy to point to the organization as the reason why success wasn\u2019t achieved. Unfortunately, this is only half the picture \u2013 the harsh reality is that we, as managers and analysts, all too often carry a large portion of the blame. In an ongoing straw poll held by the author across more than 1,500 people, only a handful ever feel that they could quantify the value they were creating through applying business analytics. When even the experts can\u2019t explain why analytics is important to the organization, what hope does a layperson have?<\/p>\n\n\n\n
The value of analytics lies in its ability to deliver better outcomes. And, when it comes to selling the value of business analytics, four mistakes stand out above all others as the biggest blockers of success.<\/p>\n\n\n\n
Data Quality<\/strong><\/p>\n\n\n\n Business analytics solutions are intended to compile data across the organization and make it available at all levels. In this way business analytics serves as a mirror, effectively shining a light on any data quality issues that exist. Business analytics solutions, if properly implemented, can resolve some quality issues but it is not feasible to retroactively resolve all data quality issues for use in dashboards and reports.<\/p>\n\n\n\n Resolving data quality issues can be difficult. Responsibility for data quality resides across the entire organization, and therefore will require an action plan that includes the entire organization. Here are some tips you can use to begin to get a handle on your organizations data quality.<\/p>\n\n\n\n There are too many potential causes to list them all, but here are a few common problems to look for.<\/p>\n\n\n\n Data quality refers to the level of quality of Data. There are many definitions of data quality but data is generally considered high quality if, “they are fit for their intended uses in operations, decision making and planning.”. Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as data volume increases, the question of internal consistency within data becomes significant, regardless of fitness for use for any particular external purpose. People’s views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose.<\/p>\n\n\n\n Characteristics<\/strong><\/p>\n\n\n\n Steps to address data quality<\/strong> – To ensure data quality, the business intelligence project team has to address it from the very beginning. Here are several significant steps to consider:<\/p>\n\n\n\n Enterprises must present data that meets very stringent data quality levels, especially in light of recent compliance regulations and demands. The level of data transparency needed can only result from establishing a strong commitment to data quality and building the processes to ensure it.<\/p>\n","protected":false},"excerpt":{"rendered":" The intersection of statistically based insight and the realization that information can be an asset has had and will continue to have serious reverberations in the business world. Being smarter has always meant being successful; as far back as the 19th century, analytics was already generating competitive advantage. Success requires more than just knowledge of…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-104774","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\n