FAQ

The objectives for implementing Big Data Analytics may be different based on the size, sales, geographical diversities and customer segments – but there are some common questions that we have seen several organisations trying to grapple with.

  • Ok, we know about Big Data, its all over the place – but what are the tangible benefits my organisation gets out of it?

  • There is a plethora of Data Analytics offerings – which solution would suit my organisation?


    There are a number of Data Analytics offerings in the market today which can be broadly classified under:
    Custom made on premise software and process workflow Cloud based SaaS using paid API subscriptions While one can go into detailed evaluation of the pros and cons, a high level view is that the Custom made on premise software is a time consuming iterative process that of course has an increased impact on budgets. Due to the dynamic business conditions today where competition is forcing changes in business strategy and market outlooks almost on a quarterly basis - a bespoke rigid software however custom made loses its appeal. SaaS mostly cloud based offerings provide an initial cost advantage at the cost of being too generalised for your analytics application. There is also a possibility of it resulting in higher costs once your application goes on production despite volume discounts extended.
    At Applied Analytics we work with you to create an infrastructure that brings the best of the two worlds – of course it is easier said than done because of the complexity of each offering and the data repurposing middleware that need to be done to ensure runtime compatibility. That is where we bring in our expertise in working across platforms and technologies.

  • Are the processes and workflows of my organisation flexible enough to utilise the benefits of Big Data Analytics?

    As an experienced business leader you are aware that the success of rolling out any new technology will be measured by its outcome which in turn depends on the way existing processes and system adopt it or adapt to the new technology. This is one concern we have noted from senior executives while considering Big Data Analytics implementations. Many of your data infrastructure services are already developed specific to your requirements which while creating a significant amount of convenience for your workflow also tends to be rigid and resistant to newer technology adoption.
    Sure, this could be a hurdle in your migration to Big Data – but as a astute business leader you know that this short term concern should not let hinder the ultimate benefits your organisation can get from Big Data Analytics. At Applied Analytics we work with customers to suggest analytics platforms that are highly flexible and customisable so that the existing process and systems could easily adopt new technology introductions.

  • Why, we already extensively use Data Analytics – what is the need of this big technology?


    True, Data Analytics is not a new concept and has been there since data was invented. The only difference is how we tend to process data to derive insights. Data has been traditionally stored in databases in a wide variety of formats, its held in large storage in multiple data centers. This has made data processing not the efficient and not at all real time in nature. Anyone who has run data analytics on historical web logs or bank transaction history will know how tedious a sorting it could be. So applying multivariate data analysis on such data was near impossible task. So we had to be satisfied in taking a certain sample data out based on certain statistical rule and apply data analysis and hopefully derive a proper inference. No wonder that such inferences did not bring in credibility. As probabilistic uncertainty multiplies with the levels of uncertainty error variance increases in magnitude with reduction in sample size. And increasing sample size increases the computation time and corresponding cost. This is the major difference between your old data analytics and the Big data Analytics technology.
    At Applied Analytics we work with clients to understand traditional data analytics tools before we recommend a solution. The solution we suggest and help implement takes into consideration the technology available with you, the front end utilities and your resourcing base so as to make the least impact die to migration to Big Data.

  • Well, we already have invested heavily in a robust time tested Data infrastructure – why do we need to change?


    Storage of enterprise data has been done in data centres either in house or external pretty much in a similar way over the last two decades. Data has been stored and retrieved on demand or for batch processing which albeit slow has catered to our modest analytics requirements in the past. However it is not so now. With global competition and fast changing customer demands enterprises have to change dynamically to market changes. The question is how fast can it measure these market changes and how soon can it turnaround its processes to address the changes. Therefore enterprises need to get more detailed data, process it faster so as to provide faster insights to its stakeholders. In these days therefore static data loses it significance over dynamic data inputs better known as data streaming. Performing real time analytics on such data is the challenge that will provide faster real time actionable insights to the decision makers. The pace of a certain tweets and retweets in social media is a perfect case that highlights the speed and volume in which data needs to be ingested and processed to leverage on today’s technologies available in the consumer space.
    At Applied Analytics we work on such real time analytics on data streaming applications using proven and available technologies that suit the typical applications of your enterprise

  • It’s all about optimisation, isn’t it – how will it help my top line?

  • Big data – my Board will ask how I can store secure information on the cloud – will it not breach my company data policies?

    This is indeed a justifiable concern. Cloud and virtual storage is obviously the building block of all Big Data applications. By dint of its very architecture, cloud as a technology may seem to be in direct contrast to the traditional “walled garden” of data centres. Enterprise data policies have always espoused the practice of data to be stored within company firewalls so that data policies can be rigorously enforced. Cloud based data storage are pretty likely to face questions from data privacy and policy experts within an enterprise.
    At Applied Analytics we enable enterprises to categorise data based on multiple parameters as type and structure, access levels, storage locations, latency requirements, criticality, export control which would indicate the data storage methodology. A hybrid storage architecture will normally evolve which adhere to policy compliance that also optimises analytics throughput.

  • With all the processing capabilities of Petabyte data isn’t it a large investment at this stage?

    Implementing data analytics and integrating it with your existing business processes need not always be an expensive affair. We at Applied Analytics are pursuing that same objective – optimising TCO for implementing data analytics for your organisation. We are a small but specialised team who are hands on into analysis, programming and administration to ensure that you get the best value for the investment you make in data analytics.

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