Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...Full Bio
Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.
Python Opens The Door For Computer Programming
How Big Data Works: As a Success or Failure
- Knowledge: Big data is noisy and plentiful. The ability to crystallize a business problem and not boil the ocean is critical to being able to generate rapid and relevant insights, not only the one person is comfortable to use and understand the data, but it is necessarily to be understood by everyone on the same page with an explicit, company-wide model like the set of assumptions about the world or for industry. This should be continuously updated as a new data sheds new light on the real dynamics underlying your business.
- Straight Embeddedness: Make sure that data is integrated into every department and function, so that we can analyze it on the large scale, for instance with a "data" person embedded on each and every team or even better, by having data analytics be part of almost every job.
- Upright Consistency: There needs to be firm-wide norms about how to interpret data, and there prerequisites to be a reliable and repeatable set of data that the whole firm uses.
- Merchandising: Merchandising means using presentations and reports to share progress on projects, however, these should also be treated as public accountability and training sessions. CarMax, for example, holds an "open house" every two weeks. These appearances are open to anybody in the company and many C-suite leaders regularly attend and provide feedback. These sessions not only create a systematic and laborious approach to handling projects, but also help to create a culture centered on using data to move projects forward.
- Intellectual Inaction: If the company has already been successful without using data analytics, be aware that the switch will be harder for you than most. Managers will have to let go of decision-making processes that have worked for them in the past and learn how to use data analytics instead. Intellectual inaction might also be a problem if data is in a silo like if only part of a firm uses data.
- Ambiguous Results: Without clear and constant Key Performance Metrics (KPMs), management won't know how to start applying insights gained from data or be able to measure success. Even if you start out with clear KPMs, keep in mind that the process of analyzing the data may need to change. Consider leading television networks such as HBO, Showtime, ESPN and Comedy Central. When these networks introduced their apps, it unexpectedly shifted them from business to business to business to consumer. When your customer is no longer other businesses (cable companies, in ESPN's case) but individual consumers, your KPMs necessarily change.
- Complication of Glitches: In discussions, some leaders admitted that they were less likely to use data to drive their decisions when the problem was very multifaceted. However, this is exactly the time that data analytics is most useful. The key is to break the tricky down into small enough component parts so that data becomes useful once again.