Making data analytics work: Building a data-driven organization

the capabilities needed include the traditional things that people run to like technology hardware and software and what kind of applications but the truth is there's a lot of capabilities that have to be built around the organizational design people and processes because the truth is when you finish building your infrastructure and you've got big data and big analytics you've got to figure out who's going to use it how's it going to be used what kind of analysts are you're going to have do you need data scientists or just analysts you need a business solution architect or is it a simple database in addition to that how do you make sure that the data you get is good clean data you know the old garbage in garbage out still applies garbage in to a great model the model itself doesn't give great results so data hygiene data cleansing making sure data is clean and having data governance around who is responsible for keeping and securing clean accurate data that gets fed into the big data analytics becomes very very important so it's more than just the hardware infrastructure software and applications it's the people and the governance around it and we see companies and clients working towards centers of excellence and distributed centers of excellence but they focus on that kind of breadth a lot of our clients start there should we centralize our analytics so should we decentralize them I don't think that's perhaps the first question I think the first question is how do you make sure that the organization responsible for the analytics looks at their job as a Service Bureau and makes sure that they are providing useful and used analytics to internal customers there are advantages to centralizing as an example some of our more advanced data scientists you know they're their definition of a funny joke is about SAS and sequel most of us don't get that they want to be in a culture that makes them rewarded and protected but they want to see their analytics used and useful and so yes you can focus on centralized and decentralized but first we would suggest focus on are you creating the right service bureau culture are your analysts doing analytics for analytics sake or to help the business and who in the business and does that business user believe they were helped so that's a framework that we find is very helpful as companies sort through how much decentralize and how much to decentralize and it's always a mix between both there usually are four or five kingpin roles where a tremendous amount of deep expertise can be shared and in a way that helps a lot of internal constituents and customers those roles really fall into the data scientist category business solution architects campaign experts and advanced modelers those roles are really critical the business solution architect is someone who's going to really understand how to create the right big data warehouse so you can use the data and so that it's accessible and useful in a usable way the data scientists our folks that are going to really help create that advanced modeling but also they're going to be able to programmatically take those models and make sure they're repeatable and use use programming language to reduce some of the human interaction the campaign experts are the folks that that's that last mile if you got a great model but you can't turn the model into a campaign that touches a consumer or a customer you've got nothing so campaign experts are that last mile that make sure that the models get turned into results that turned into actions and those are some of the key roles that are really important to make sure that a client can leverage big data effectively and quickly

One Comment

  1. Ganapathy Prabhu said:

    My former Boss and huge supporter in my entrepreneurial journey, has written a straight-forward article on defining a Data-Driven Organisation.
    His clarity on this subject is obvious owing to twenty years of working with several organizations, including research (NASA, MIT), online giants (Facebook and AOL), Fortune 500 (Airtel, BMW, Pepsi, Goldman Sachs), and Data startups.
    I would recommend this article to any non-technical Professional or Organisation trying to understand the concept of Data-Driven Organisations or anybody in the process of the building one

    June 28, 2019

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