Data Analytics and Business Outcomes: A Discussion Worth Your Time

February 14, 2016
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What picture does a network paint in your mind? If it resembles a labyrinth— you are certainly at par with most firms, dealing with a tedious effort to get their acts together. For me— the situation is no different. I perceive most networks as winding passages— leading into the world of exploding firewalls.

What picture does a network paint in your mind? If it resembles a labyrinth— you are certainly at par with most firms, dealing with a tedious effort to get their acts together. For me— the situation is no different. I perceive most networks as winding passages— leading into the world of exploding firewalls.

That said, data mazes are certainly human inflicted and the only way to unravel them is Data Analytics. Considered as a technique for examining unprocessed data— Data Analytics helps in drawing the needed conclusions— allowing firms to take better decisions and largely improve the business model and therefore outcomes. The perfect combination would be integrating Data Analytics with the caveats of human intuition. It would enhance the overall security— helping us find a way out of this mysterious maze.

Big Data and Cohesive Data Analytics


For most entrepreneurs— business outcomes define the complicated orifice to the data inflicted strategies. Data Analytics help achieve the same— treading onto the fundamental premises of technology. It all pans down to the impeding departmental barriers— halting progresses and addressing the C-Suites. For the starters— an organization can surely make use of Data Analytics for connecting with possible business outcomes. Data Analytics, however, isn’t a solitary tool— comprising of retail, supply chain, collections and financial analytics.

  • Assess the problems in hand

It is quite tempting to fall for a new tool— without analyzing the endgame. Data Analytics helps us determine the best bet for the business by processing outcomes and feasibility. Risk Analytics is what comes handy here.

  • Unifying strategies to the best of capabilities

We all strive for contexts and this step will certainly set the ball rolling. For business outcomes to be yielding— the data strategies need to be put into the context. This can only be achieved by amalgamating business practices with data inputs. This is where business analytics come to the rescue.

  • Data Team and the Position

A common pitfall is to undermine the contribution of the data team. Even the most data-driven organizations miss out on contribution of data analytics. For me the Data Team must be implemented as the ‘Centralized Service Organization’— achieving the bi-model data. This collaboration will be beneficial in the long run— with multiple inputs from research optimus—striking the perfect balance between workforce and workload.

  • Taking down the organizational barriers

This includes breaking down of the rigidities and understanding granularity of the data. With data analytics on board— one must look to be proactive and embrace a cohesive approach.

  • Embracing the Changes

Business outcomes are segregated into industrial shifts, customer expectations and even business priorities. Agile working methods should be adopted— most of which are flexible and easy to play with. It requires entrepreneurs to be cognizant of financial analytics, retail shifts and even collections analytics— each helping firms to achieve desired outcomes.

  • Changes need to be Managed well

We do need to take care of business intelligence in order to make outcomes look lucrative. Hoping to democratize the use of analytics tool will only take us this far. For better results— user adoption must be taken seriously. Incentives need to be restructured and the newly adopted ‘DOMO BI’ applications have to be embraced. In a business— changes are inevitable and data analytics will only help us manage them better.

  • Critical thinking will win it for you

Looking at a piece of information might appeal differently to individuals. People draw different conclusions but it only helps when someone thinks out of the box. Right questions need to be asked and even data analytics need to be critically gauged.

  • Details are always important

Companies mature with time and it requires someone with an eye for detail. Earlier the companies were only restricted to website changes and bigger ones like the entire company structure. Now the company needs to keep a track of sales upticks— coming from the remotest of regions.

  • Starting small and growing tall

It is a cardinal sin to overinvest. Expediting success it a right way to think but it’s always right to work up the zenith and not to think you are already there. Limits need to be implemented and this is how data analytics can be useful.

  • Dividing Labor

If you are looking to leverage AI and machine learning— tools, data scientists and other team members need to be included— briefing each of their duties. Big Data is something we need to work with and this requires better analysis and proper division of labor.

  • Accepting Failure

Failure isn’t a bad thing if it’s accepted. One must make the best of data analytics and learn from the mistakes.

  • Data Torturing

Data being used in a non-scientific manner isn’t usable like 123movies app. Torturing data in this manner can lead to catastrophic results. Data Analytics offers a systematic manner of data usage and therefore exciting business outcomes.

With business outcomes taken care of— the onus is one the ‘man machine’ juncture for offering better options pertaining to safety and security. Information is extremely critical and data analytics do maintain the confidentiality of the same. While it redefines the concepts of trading— the inclusion of cloud computing has taken data analytics to a whole new level.

Do watch this post for more on the association of data analytics with the trending cloud computing norms.