Getting Future Ready — Decoding ML Strategies
Disrupting End-to-End Data Management Value Chains
We all have heard this before — “Man proposes, God disposes”.
Let’s recall those plans and roadmaps we had crafted for our respective organizations, exactly a year ago, 2020.
As fate would have it, pandemic struck, the mightiest calamity ever occurred for this century. Call it nature’s fury or vengeance with a bang, for tampering with nature, we had to face the consequences.
Unexpected, Unpredictable, Undesired, Unfortunate.
Back to the present — March 2021!
Overnight disruptions forced us to be adaptable, to be responsive, and to be resilient.
The pandemic impact continues. We see disruptions across societies, markets, industries. It is the sheer positivity in attitude, determination to adapt, to thrive in the face of change, that we did, and have done, will be doing, revise our plans, our organizational strategies, and focus on the technology trends that will drive our business goals down the line.
The COVID-19 crisis has only accelerated the pace of digital transformation on the part of organizations across the sectors, industries. The abrupt disruption has led to varied technology trends that emphatically promote hopes in response to the turbulent situations of the previous year.
As our peer Scott Buchholz, Emerging Technology Research Director and Government & Public Services CTO, Deloitte Consulting LLP speaks, “For data, we investigate how leading organizations are industrializing their AI initiatives with “MLOps” and, consequently, developing new approaches to managing data for machine, rather than human, consumption. We also discuss emerging trends in cyber security.”
New technology. New business plans. New Promise. New tomorrow.
This is called resilience.
One noted ingredient- DATA.
Past, Present, Future — data form the core!
And data hold the focal point in the whole process. Because data remain a key asset for all the decision-making process. Any planning, any roadmap, we need data in real-time and the analytics and digital technologies help us leverage these data for our purpose. Let’s accept one fact–the data we see, that is obvious before us, are not sufficient for an advanced level of the data-driven decision-making process. We need to analyze in more capacity than the obvious data present! The massive non-traditional data, unstructured data, lying somewhere in databases in the legacy system, need to be tapped as well! How? Arrange them, aggregate them, and store them in some CLOUD-based systems that are optimized for Machine Learning (ML).
But How?
Challenges
· Traditional form, or the legacy form of organizing data prevalent today
· This form not compatible with a future form of AI-based decision-making platform
· Greater latency due to legacy systems of data models
· Delaying in the ‘executive –decision-making process’ due to latency
· A wide gap between digital non-native and digital native participants
Solutions
· Re-engineering the model of capturing, storing, processing data
· Deploying Advanced data capturing, structuring capabilities
· Deploying analytics to locate, identify the connection among random data
· Next-generation data stores on Clouds that’d support complex modeling
Capturing, Storing, Processing data the ML way
To leverage the AI capabilities and ML algorithms towards decision-making processes, data handling needs a complete makeover and structure. All the data existing in the legacy system, untouched, real or distorted, lost in some shelves, data whose identity sources are not known, need re-engineering and remodeling of the entire structure. They need to re-engineer the entire data management value system
The areas where organizations need to focus on their re-engineering efforts are-
1. Capturing, Storing of DATA
Troves of data are lying in dormant forms, valuable data they are! They may be in the form of traditional enterprise data wrapped inside files databases, systems, organized in the legacy format.
Data are getting generated every moment.
Today, with the prevalence of digital technologies like the IoT, Analytics, AI, sensor-based ecosystems all are capturing data in one form or other. Mobile phones, audio-video carriers, carry data wherever they go. Nontraditional, unstructured, raw formats are also lying somewhere that needs to be tapped for future ML-based decision-making processes. Predictive analytics depend on all traditional and non-traditional data from past to the present paving way to the future!
These critical data need to be captured for future based predictive analysis, which may turn out to be helpful for organization development and contributing towards ML-based decision-making capabilities.
So, today, instead of storing data in the structured, clean form of rows and columns, tables, organizations are rather focusing on the collection and storage of herculean volumes of unstructured data, to make them ready for ML algorithms and advanced analytics tools. These data would be collected from a variety of advanced databases related -technologies like social media platforms, IoT devices, AI-based platforms, and so on.
Some modern databases where unstructured data can be stored are –
· Feature-Stores: In the future, an organization will be following different data models working separately, independently of each other, and in parallel mode. All of these data models will have unique feature sets to operate and function. This type of database will help manage data efficiently, as well as assuring scalability while reducing decision- costs. AI-capabilities will help predict specific demand types on the part of unique data models working differently, independently but as neighbors!
· CLOUD-based Data Warehouses: The future trend is predicted. The forecasts say it all — a massive, in-value US$23.8 billion for the Cloud-based Data-Warehouse- as-a-Service Market. The feature works like aggregating massive data from varied sources in an enterprise and providing it to end-users for some real-time data processing and mining activities.
· GRAPH-Databases: In the traditional database-models, unstructured data are unsearchable, unmanageable. Analyzing highly interconnected data relationships arranged in a traditional model of tables, rows, to their maximum potential becomes a challenging task.ML & Advanced-Analytics will provide a modern fault-tolerant, self-healing, type of data-architecture that will require minimal maintenance, thereby reducing huge repairing & administrative costs.
· Time-Series-Databases: This feature has already arrived two years back. This type of databases track data and record their specific time of creation in the form of a unique identity-insert to a datasets.
2. Discovering and Connecting DATA
Here’s the good news for organizations especially bigger enterprises where non-obvious data are scattered, unexplored, unexciting!
Machine-learning(ML)-Powered Cognitive Data-Steward-Technologies do this job of discovering or locating these non-obvious data, giving light to their insights, their connections, their in-depth details. How will they do that? As mentioned below-
· Costly stewardship processes that data scientists are engaged in now, can be done automatically in the future, with the help of cognitive technologies, analytics, and semantic models. This way, these data scientists will get the scope of focusing on doing research work on greater analytics models (will be freed from their mundane tasks!).
· Can identify similar data codes and coming out with custom data algorithms that can apply in various data models.
· Cognitive Data-Stewards will help manage and enable greater efficiency towards data management.ML capabilities can do the major task of processing the MASTER DATA. The entire mechanism enables users to visualize relationships among data. Efficiency is assured and so the data readiness and quality!
3. Amplifying ML Opportunities Reducing Latency
Organizations want to explore ML capabilities in all forms when it comes to data. They want to make and take significant decisions just at the starting point when data are being entered into the network! They want to decide the data-related crucial matters before putting them or sending them to the Clouds. How?
· Edge Computing — In the process, they want reliability and efficiency via Edge Computing. Yes, they want to leverage the immense capabilities of Edge Computing which are considered to be garnering the aforementioned benefits as well as reducing a high-level of latency. When deploying ML algorithms, Edge Computing is very useful because the former need real-time and uninterrupted access to a vast amount of the recent most data.
· Advanced Connectivity 5 G-Making a real-time decision at the first point of entry of data, advanced connectivity acts as an enabler. Since the present generation connectivity like the 4G or LTE or Wi-Fi connection are not fully capable of reducing the latency level and are limited by bandwidth besides other limitations such as the number of devices they can manage. The advanced connectivity in the form of 5G promises milliseconds latency, and very fast speeds, and can stretch its bandwidth capacity so that it can manage many other devices per square kilometers simultaneously.
Future Beckoning, towards ML, and beyond!
Machine-Learning (ML) initiatives, MLOps, will be gaining momentum in the future like never before. In the coming 24 months and beyond, organizations will be handling challenges the ML way. They are very much realizing the fact that to make strong data-driven decisions for their organizational goals, they need to analyze non-obvious data as well. To become AI-fueled organizations, the latter need to deploy, integrate Artificial Intelligence (AI) and Machine-Learning (ML) into just every activity, every process, and every system, consistently and of-course at scale. Amplifying ML capabilities will also mean organizations, enterprises will be industrializing Machine Learning via MLOps. All these are the expected trends that will rule in the future. Data are paramount and a leading force behind a company’s growth, generating ROI, and obsolete data, outdated data infrastructures, will act only as a deterrent to achieve those goals.
Much to the amazement of the business world, the current ML technologies market is rapidly expanding at a pleasant rate of 44% annually. What more, this market is expected to reach 8.8 Billion USD in value, by the year 2022.
End-Note
Realization dawning upon us, and as we continue our journey towards digital transformation, we are enlightened towards re-engineering data strategies to craft the future of our respective organizations. We are accountable for what we feed our machines, and how we deal with data and the entire ecosystem down the line. Simple understanding, how an enterprise with 100 years old legacy data will retrieve those non-obvious data to use for decision-making processes in the future. Connecting the past to the future via the present road is what ML revolutionary trends all about.
Source
Paul, P., Irfan, S., Sandeep, S., & Tello, J. (2021). Tech Trends. Retrieved from Deloitte: https://www2.deloitte.com/us/en/insights/focus/tech-trends.html
