APPROACHES TO ADOPTING COMPETITIVE DATA ANALYTICS 4
Approachesto Adopting Competitive Data Analytics
The greatest challenge to making the shift to analytics-basedcompetition entails building processes that enable business leadersto trust in the data. Managers face a challenge in ensuring thattheir reliance on technology does not destabilize their earnedrespect from experience (Otten et al., 2015).
Although information management is a critical element in creatingrobust analytics, there is a need to adjust the cultural normssurrounding decision-making. A successful analytical implementationstrategy integrates both the technical and business sides of anorganization. It stipulates how groups should work together and givesthe reasons behind given organization design (Lillegraven, 2014).
The first strategy to eliminate doubt on data analytics andtechnology is by creating awareness and responsibility. Increasedreliability of information by managers necessitates further knowledgeon where to find data, how to find it and the conditions to ensurehigh quality. However, higher awareness calls for greaterresponsibility. Consequently, administrators should participate inactivities that facilitate data compliance as a strategy to improvethe value of information. For example, the establishment of datacouncils aids to address organization-specific challenges such asexcellence and data governance (Otten et al., 2015).
The second strategy calls for manager’s openness to ideas.Successful implementation of data related technology is associatedwith multiple analyses since organizations are rarely aware of whichresults can work efficiently. It is also linked to humongousanecdotes that fail to provide a ‘eureka’ moment. Consequently,firms should adopt a broad range of ideas as a strategy to cultivatecompetitive advantage in the form of innovation. Openness to newideas challenges the status quo and provides acceptance to theassociated mistakes (Pourshahid et al., 2014). Finally, executivesshould provide signals about the importance of analytics to theiremployees as a strategy to initiate a cultural change. Workers arekeen to identify cues about the essential management values and thepossibilities of the virtues to last. Institutions should establishdata labs and centers of excellence to confirm the seriousness of theenterprise to accept data (Otten et al., 2015).
Lillegraven, T.(2014). Untangling the ambidexterity dilemma through big dataanalytics. Journalof Organization Design,3(3) 27-37 Retrieved fromhttp://research.cbs.dk/en/publications/untangling-the-ambidexterity-dilemma-through-big-data-analytics(43f29114-5e05-4f44-848a-b4f9610bcfbe).html
Otten, S.,Spruit, M & Helms, R. (2015). Towards Decision Analytics InProduct Portfolio Management. Journalof Decision Analytics,4(2)1-25 Retrieved fromhttp://link.springer.com/article/10.1186/s40165-015-0013-7/fulltext.html
Pourshahid, A.,Johari, I., Richards,G. & Amyot, D. (2014). A goal-oriented, business intelligence-supported decision-makingmethodology.Journalof Decision Analytics,9(1). Retrievedfromhttp://link.springer.com/article/10.1186/s40165-014-0009-8/fulltext.html