Disruptive Data Science Being Unleashed by Three Technology Trends
Disruptive data science has the potential to transform any enterprise into a big data-driven enterprise.
The future-proof digital transformation introduces you to endless possibilities of business opportunities through data and analytics. In order to make this pragmatic transition to digital enterprise happen, the chief information officers, chief data officers, and data & analytics leaders will have to make a paradigm shift towards the futuristic data science. They need to be proactive and start leveraging the cutting-edge business applications and analytics applications.
The thought leaders should make their data disruptive. They need to consider data as the raw material for any decision and start building consensus. The data is no more a low-hanging fruit; instead, the enterprises should realize the data exists everywhere (at rest, in motion, on-premises and in the cloud) and should find a way to get more eyeballs. Be the first to market the big data which continues to grow rapidly in all three dimensions.
Data science is a use-case-driven, game changer, monotonous, and dexterous analysis of granular data. Big data is adopted with an intention of procuring insights and integrate them into down-stream applications.
In order to have a win-win outcome, the business head honchos should have a helicopter view and bank on the seamless scopes that data science administers. Gone are the days of the old school way of keeping siloed data. Now, data and analytics are the nuts and bolts of the digital business fabric and they should be on every aspiring business leaders’ radar. Data science is progressive and can play a dynamic role in empowering the entire organization, not just reflecting where it’s been.
At Athena, we are passionate about leveraging big data so as to evolve the way maintenance decisions are being made and ladder up the data and analytics per se. We also believe that smarter and abrupt use of data retains the potential to transform maintenance in companies especially hailing from the mining industry.
According to McKinsey, by unlocking the dormant data, the mining industry could generate an economic impact of $100 billion per year in maintenance alone. Click To TweetThree technology trends that disrupt data science
#Trend 1 – Debunking the Value in Big Data
Extracting value from data is becoming just as important as extracting resources from the ground.
Big data articulates volume, assortment, low remissions, and ubiquity, whereas data science endures new terminologies such as machine learning, anonymization, Avro, CDR analysis, parallelized and in-database algorithms, predictive modeling, Chukwa, Map Reduce, and model operationalization to name a few.
Data scientists in mining have recognized that there is a colossal space to work on operational efficiency in a secure and cost-effective manner, provided their team can access, understand and apply the big data to real-world situations. With the advent of the cloud-based predictive maintenance software, it has become easy to seize and analyze an operation’s condition monitoring data and then derive its entire value through insights, actions, and outcomes at an elevator pitch.
By allowing consumers to seize and run dynamic analytics on high-frequency data, the data science has indeed blazed a trail. It has made the ecosystem disruptive by helping the mining industry cut through the noise and transform an ocean of sensor data into operational business intelligence that drives results.
#Trend 2 – The Ubiquitous Intelligence
Next-gen cloud-based apps will be able to provide real-time entrée to insights that will enable thought leaders with better decision-making approaches anytime, anywhere.
Did you know you can run Python in your smartphone?
There is an app developed every day in order to satisfy the customers’ requisites. The global mobile apps market is expected to grow at a CAGR of 15.14% by 2021. The trend which is rapidly gaining momentum in the market is the integration of chatbots in mobile apps. Smartphone users now do not want to miss any opportunity of having chatbot in a mobile app. People don’t classify experiences, instead, they are ushering in the expansive uptake of mobile apps to make their lives simpler.
Based on the users’ feedback, data science providers have recognized maintenance and equipment health management as the sweet spot for the development of mobile apps using big data. To meet this requirement, data scientists offer state-of-the-art android apps that enable mine operators to access, understand, and take necessary action based on their equipment’s’ condition anytime and anywhere.
# Trend 3 – Next Era of Human-Machine Partnership
Predicting 2030. Global tech magnates have predicted the next era of human-machine partnership and how they’re intending to prepare for their future generation of 2030.
Thought leaders from around the world have predicted on the emerging technologies, especially artificial intelligence, Internet of Things, machine learning and data science and they’re going to revolutionize our lifestyle in a manner which we have only seen in fictional movies like Minority Report. As per their forecast, 20 years down the lane i.e. by 2030, our future generation will witness the human-machine partnership. To make this happen, the data scientists and AI experts have already started gathering powerful insights that will enable businesses to navigate the future.
2030 Digital Transformation Prediction - 1
| Lifestyle | % of Chances |
|---|---|
| Automated systems will drastically reduce our time consumed in work | 50% |
| Advanced & affordable healthcare monitory devices will be available. | 46% |
| Information will be derived and managed in completely several ways. | 54% |
| Smart machines will replace humans & connect our lives to tailored goods & services | 43% |
| People will be all the time connected with technology | 42% |
2030 Digital Transformation Prediction - 2
| Work | % of Chances |
|---|---|
| More collaboration will happen | 49% |
| More job satisfaction by offloading the tasks to intelligent machines | 42% |
| Schools will prepare students for jobs that don’t exist yet | 56% |
| Candidates will be trained on the job with AR | 46% |
| Industry will be completely revolutionized | 50% |
2030 Digital Transformation Prediction - 3
| Business | % of Chances |
|---|---|
| Clear protocols will be established if autonomous machines fail | 50% |
| We'll be more likely prone to cyber-attack | 48% |
| Computers will determine between good and bad commands | 45% |
| We’ll be globally connected and part of remote workforce | 49% |
| Technology will connect the right person to the right task, at the right time | 41% |
The data science service providers have gone the extra mile to make the disruptive big data ubiquitous irrespective of the fact that it is still on the bleeding edge and we’re on the cusp of 20+ years of human-machine partnerships and not in it. Essentially, this partnership is only going to become deeper, stronger, empowering and more immersive than before, thereby helping us surpass our own limitations.
Conclusion
The mantra to developing successful data science solutions especially in the mining industry is keeping the engineers and analysts central to the process. Miners are spending huge bucks on technology in order to gather mammoth-sized data. However, the downtime and cost involved are always far-reaching and they are not always able to implement the analytics in an effective manner.
Hence, it is important to approach any seasoned big data service provider, who is well versed in pro-integrating the big data analytics technology with real-life maintenance scenarios. The expert data scientists can help your operators make smarter and more cost-effective decisions.
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