A sports-car is as fast as its pit-crew. A highway is as good as its smallest crack. A great painting is as beautiful as the details from its minutest brush-stroke.

Similarly, a truly revolutionary image analytics solution is as awesome as its tiniest detail. You do not just utter these words and stand across an extraordinary solution. You have to build it. For the needs you aim for. For the risks you are fragile for. With the right partners.

Image analytics works great when it can address three salient areas with comprehensive clarity, readiness and confidence.

Its problem-orientation, its data and associated training parts, and its strength to resist the possibility of going awry.

Factors to consider when building an image analytics tool

First things first. Do not slap AI and Image Analytics on your environment without a clear need and goal. It is supposed to deliver insights. Insights are needed for a specific reason, a unique gap or a particular decision-scenario. Without the right direction, no matter how great and fast you can conjure up images or analytics, they would be heading towards a giant wall. They would be going nowhere. You do not want to waste efforts, resources, and talent on chaos and confusion.

Next, do not expect miracles from any system – no matter how brilliant it is – unless you feed it the right data-sets. Marketing insights or cues for predictive maintenance – they cannot come out of data that is slippery, knotty, irrelevant, or unfiltered. Any algorithm or analytics model will spit out only bad insights when it inhales bad data. So never compromise on training the system with the right quality of data. There have been many instances where a system has proven a failure due to confusing or unclean data injected into it. In fact, recently, a small subset of ImageNet, an industry-standard database containing more than 14 million hand-labeled images in over 20,000 categories reminded that noise and manipulations can confuse even the most natural dataset, and 98-percent of the time. So do not take data accuracy and quality for granted. The tools only work on the kind of data that you supply them.

And finally, avoid the tempting lanes of bias, discrimination, intrusion, and irresponsible power. Image analytics is wired with the innate power to ‘tell’. It can arm your organization with unprecedented information. But it is you who has to make sure what boundaries and ethical compass would be in place to guide this power and to control its exploitation for the wrong reasons.

These concerns are actual challenges that have been iterated at many points. Reflect on what a recent report on the state of Data Science from Anaconda pointed out – 52% of data science professionals shared they face trouble demonstrating the impact data science has on business outcomes. This problem is the highest for healthcare data pros (66% sometimes or never can do so) and for people in consulting (29% face this). Yet, 15% of respondents had implemented a bias mitigation solution, and only 19% did this for explainability.

Also, look at how shockingly the oblivion or inertia pervades? As many as 39% of enterprises manifested no plans to address bias in data science and machine learning. There were 27% that had no plans to make the process more explainable. Another survey ‘Big Data & Analytics Maturity 2020’ by Atscale reveals that the explosion in data size and variety, coupled with the increased focus on analytical use cases, has engendered new challenges for legacy data virtualization technologies. This is stark and serious now because the need for ad hoc access to both live and historical data has been rising among business users as they leverage AI and ML for analytics. As to the challenges they have been experiencing with their analytics infrastructure, governance came up as a number one challenge followed by skills, performance, costs, and security. As many as18% shared that incomplete data is the biggest challenge with their AI or BI tools, and 15% pointed at poor query performance as a big challenge.

Follow The Needle – Plan Your Strategy

  1. It is expected that more enterprises will operationalize their AI initiatives by 2024. But users need to trust data to be sure of how the model works, how complex it is, how opaque it is, etc.
  2. By 2025, data stories will be the most widespread way of consuming analytics. Almost 75% of those stories will be automatically generated through augmented analytics techniques.
  3. By 2023, more than one-third of large organizations will have analysts practising decision intelligence, including decision modeling.
  4. By 2025, AI for video, audio, vibration, text, emotion, and other content analytics can spur significant innovations and transformations at more than three-quarters of Fortune 500 companies.
  5. By 2023, organizations that use active metadata, machine learning, and data fabrics to dynamically connect and automate data management processes will reduce their time to data delivery and impact on value by 30%.
  6. By 2023, companies using blockchain smart contracts will increase overall data quality by 50%, but reduce data availability by 30%.

Also, Graph analytics will strengthen rapid contextualization

So you see, it is not that simple to eke out insights from analytics and AI. But it is totally feasible – and strongly advisable – with careful attention and meticulous design.

Dot the i-s, Cross the T-s: How to ensure right implementation of image analytics

Building an Image-Analytics system that is free from risks and dead-ends is not an easy task. But it is not an impossible one too.

Here are some specific points that you should keep in mind to ensure a solid, and outcome-rich, image analytics system:

  1. Stay focused on data quality, access, and cleaning
  2. Add context and accuracy when it comes to datasets
  3. Apply expertise and strong tools for image recognition and classification
  4. Train the system the right way
  5. Work on model replicability
  6. Aim for accuracy and reliability
  7. Ensure strong and safe machine-human interface
  8. Work on easy and seamless roll-outs
  9. Do not side-step scalability
  10. Embed foresight for security and compliance
  11. Inject skills, training, and human capabilities
  12. Avoid opacity – both in terms of data and how the AI model works – as much as possible

All these areas are paramount. They remind us of the role a good solution-expert can play with Image Analytics. An able partner and team will help you in averting all dark spots when you are configuring an image analytics tool. These experts will enable you to:

  1. Define the right problem statement
  2. Customize and configure the solution for your unique gaps and needs
  3. Support on all adjacent areas of data cleansing, training, skilling, and modelling
  4. Bring best practices from their versatile experience-curve so that you can leverage the right choice that others have made, and also avoid the mistakes others have run into
  5. Monitor, manage, and help with timely course-correction
  6. Update the solution with industry innovations and creative ideas from time to time
  7. Translate insights into decision-enablers by using simplification and agility
  8. Ensure safety, security, and compliance as appropriate
  9. Consult, guide, and support – at every point where needed

By all means, the strong potential of, and the superior insights that, a good image analytics solution can offer are alluring.  But build the solution with a practical and learning-oriented mindset.