Have tools and team, but ...
still struggling for

Extracting business value out of data is not a simple process.

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I am supportive and patient ...

I've already invested a million $ so far on data science / machine learning. It's been, what, 6 months, 9 months, 12 months - my Data scientist team has been "working" on your intricate and complex enterprise problem - but despite assurances and some encouraging signs - i'm still far away from cracking it.

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Do I keep throwing people and money?

Things are building up, but it seems the team needs more time. I may continue to invest another million $ this year.

I sometimes feel, is this the best way forward?

Do I just keep throwing people and money at the problem?

Maybe it's just time. I understand, even some of the biggest and best companies in this area took more than 10 years to solve some of the biggest challenges.

10 years!

Am I prepared to wait (and continue to invest) that long?

Will my market wait for me?

Will my problem remain the same?

Will my team persist with me during the whole journey? On-boarding and retaining such incredible talent sometimes is a headache.

Answer struggle re-purpose

Finding business value out of data is not easy.

Creating business value out of data is not a simple process. Its not enough to simply push Data scientists and technologists at the problem. JetFerry's Answers Journey was designed specifically to help enterprises get more effective and robust answers - minimizing the pain of false starts, costly experiments and process back-fires. We strongly recommend leveraging proven expertise where available.

Leverage a complete end-2-end tools based approach rather than an experimental one. Go complete and comprehensive AIO (all-in-one) - get a complete end-to-end solution, rather than spending months and months overcoming integration challenges amongst vendors.

Partner with experts in providing enterprise answers, rather than transactional ones.

Partner with experts
in providing enterprise answers, rather than transactional ones

Why don't I use a proven solution?

We did a survey on why companies do not move to proven solutions. Here are the results.

Answers struggle results

1. Strategic (31%)

Building strategic capability in-house for Predictive Analytics was one of the biggest reasons mentioned in the survey. In these times of lean organizations and core competence, this seems a little contrarian. Typically, management/leaders and data scientist views and focus differ significantly.

2. Control /Trust (27%)

This was the second most reason cited in the survey at 27%. Either enterprises did not trust or agree with the vendor approach. However, there are many ways to ensure your provider is trustworthy and able to keep data confidential. Also, having a tried-and-tested methodology such as JetFerry.ai�s 4Cs helps ensure relevance, weightage and impact of the insights found.

3. Biz Domain Knowledge

In-house technology and AI teams are generally composed of technology driven professionals - focused on technology, algorithms, frameworks and the lastest tech jargon. Their understanding of the business domain is, generally, specious. They are battling on many fronts at the same time - AI and business. Both are continuous efforts requiring sustained investments of time and energy.

AI solution frameworks have the advantage that they have already "figured out" one part of the puzzle. They can focus all of their considerable expertise, knowledge and experience on the biz domain. In addition, they can leverage their experience of related offerings to provide higher value solutions.

4. Lack of clarity

Despite the hype around it, AI and predictive analytics is still a relatively new area. So, it's quite reasonable that a lot of people do not have clarity on what to expect. Many still think of it as reports and graphs coming out of their system. The reality is that AI-driven Business Intelligence has proven itself as a business enabler. It finds answers to your business answers just by meticulous analysis of your data. The 4Cs methodology from JetFerry.ai - will help you achieve consensus and clarity on the nature and size of the problem and the contours of an insight that will impact your business in a meaningful manner.

5. Don't want to experiment / Not ready

No one wants to be the first "guinea pig". They don't want to be one on whom all the experiments are being done. This is one of the biggest reasons why going with a proven solution makes more sense. Your risk is automatically mitigated as they have already crossed the inexperience bridge. They are best placed to be your partner in your Answers Journey.

Partner with experts in providing enterprise answers, rather than transactional ones.

Now, imagine using a complete
end-2-end solution to help you

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Kickstart your Answers Journey!

JetFerry.ai's Answers Journey delivers:

  • see and understand the different aspects of your problems without getting into the technology aspect
  • Insights generated out of data analysis in real-time, rather than hit-and-miss
  • Mature frameworks and tools to understand data - all automated, customizable and adept at various AI approaches - ML, Deep Learning and Cognitive analytics
  • A complete end-2-end solution that covers the whole process within a single umbrella.
  • Lower dependence on individual capability and more focus on tools and process.
  • Lower investment with higher ROI

Quick Overview

>> Have been patient and invested $1mn in tools and team, so far
>> Continue to be patient, but for how long do I need to do this?
>> Would consider a proven solution, if my concerns are addressed.
>> JetFerry.ai's Answers Journey delivers enterprise-class results continually, not just transactionally.