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Digital transformation extends beyond adopting new technologies — it goes as far as you want and need it to go. Your business has been undergoing transformation all along, and the term "digital transformation" was only coined in the early 2000s.
While you have many business assets, the data and analytics you have acquired are one of the pillars of digital transformation. Moving away from legacy systems or adopting new technologies depends on data, as does refining your business model.
Drivers That Ignite Digital Transformation
Digital transformation signifies the natural progression of your business’s growth and lifecycle. Consider how your market fluctuates and changes, how your customers change their purchasing habits, and how your client engagement acquires more depth and diversity.
Digital transformation resembles a city constantly growing in meaning and purpose. San Francisco was a port city destination for the Golden Rush in the 1900s — the city’s only distinction. With time, it turned into the key tech and investment hub, forming Silicon Valley and becoming home to educational institutions like the University of California and UC Berkeley.
With that in mind, digital transformation can start with tech, but ultimately, it involves adjusting your business culture, client experiences, and business decisions.
Data Analytics Lays the Groundwork for Digital Transformation
Data analytics seasons digital transformation in multiple ways due to its different ‘flavors.’ To fully appreciate the effect data analytics and digital transformation have, let’s explore the various touchpoints this synergy creates.
See the Business Context
Descriptive analytics helps you look back on your business endeavors through a historical lens. You have a record of past trends and performance in your business context. It’s like answering the question of 'What happened?’ and putting everything into perspective. You’ll be able to incorporate better strategies, improvements, and implementations in the first place once you’ve laid out the facts in consecutive order.
Join the Dots
Now that you have a baseline of past trends, successes, and failures, you can use diagnostic analytics to understand the whys and wherefores. This analysis allows organizations to identify the factors contributing to either successes or failures and learn from those experiences.
Forecast Business Trajectories
With predictive analytics, you can more accurately anticipate market shifts, customer needs, and operational challenges during digital transformation. Predictive analytics uses historical data, machine learning, and statistical models to predict future trends and behaviors.
Speed Up Desirable Outcomes
Prescriptive analytics taps into the very essence of business outcomes by projecting the end results. Given the context of your data material, you can potentially maximize pricing, resource allocation, and risk management or show how this or that business trajectory plays out.
More to Data Analytics with AI
You can give an interesting twist to your data analytics through AI. This can make a valid contribution to your digital transformation pathway.
Detect Social Signals
Cognitive analytics employs artificial intelligence and natural language processing. It mimics human reasoning to uncover deeper insights. Additionally, cognitive analytics can define sentiment levels and trends that traditional analytics cannot capture.
Our Coherent Solutions team spent 18 months designing a speech analytics solution for the client’s call center portfolio called Spokn AI. This solution:
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automates call analysis by transcribing voice recordings into text
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summarizes conversation content
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identifies key topics
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determines sentiment
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highlights reasons for customer satisfaction or churn by analyzing meaningful words, phrases, and emotional tones
Spokn AI uncovers customer opinions on products, evaluates agent performance, and measures quality standards in conversations. The solution provides aggregated analytics, enabling MaxContact to make data-driven decisions, improve service quality, reduce costs, and enhance compliance.
Decrease Time for Analyzing Vast Data
Augmented analytics automates data preparation, analysis, and insight generation. This reduces manual processes, allowing business users to access actionable insights without requiring deep technical expertise.
One of our clients needed a mobile health platform to improve healthcare for expectant mothers by helping future mothers manage pregnancies and receive health risk alerts healthcare providers can quickly act on. Coherent Solutions focused on predicting user churn based on app activity. Our team:
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conducting exploratory data analysis (EDA)
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performed data wrangling on raw datasets
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trained machine learning models to predict churn
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developed a scalable API on AWS to ensure secure data transmission
The solution has improved customer retention and enhanced the user experience through personalized interactions. Additionally, our solution can easily scale and can be accessed through API on the AWS platform if our client even needs to expand their service or integrate with other tools.
Fine-Tune Any Existing Processes and UX
You can actually develop a very narrow yet powerful tool using machine learning, a subset of AI. Such algorithms help you automate the analysis of vast amounts of data in digital transformation and uncover insights faster than traditional methods would. The overarching theme is that every model you build will be able to learn and adapt to produce accurate results.
One client of ours helps businesses succeed on Amazon Marketplace by providing strategy, management, and operations services to optimize sales and profitability. Coherent Solutions developed a way to evaluate the advantages and disadvantages of any product on the Amazon marketplace. Our team:
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used scraping techniques to gather training data
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created a dataset of ~250k customer reviews
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conducted exploratory data analysis (EDA)
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trained a model using BERT
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deployed the trained model on AWS
Consequently, the solution gives customers deeper insights into product strengths and weaknesses, simplifying and improving the marketing process.
Navigate Digital Transformation with Confidence
Discover the hidden roadmap in your data analytics alongside AI.
Input from Big Data and Business Intelligence
When you have vast data pools, you’ll need complex programming solutions to manage them and use big data to your advantage. Big data platforms such as Hadoop and Apache Spark provide the infrastructure to ingest, store, and process massive amounts of structured and unstructured data.
Think of Amazon: the company handles millions of back-end operations daily, including queries from over half a million third-party sellers. The organization uses big data to manage its vast inventory, recommend products to customers, and streamline its supply chain operations.
Business intelligence (BI) tools like Tableau and Power BI transform complex data into visual dashboards and actionable reports. BI enables organizations to track the results of their transformation initiatives, measure progress, and identify areas for improvement in real time.
Traditionally guided by intuition, Levi’s integrated Google Cloud in 2020 to gather extensive purchase and browsing data from 110 countries and 50,000 distribution points. This data-driven approach enabled the company to identify the widespread appeal of baggy jeans across various demographics, leading to targeted marketing campaigns and increased sales.
Turning Data Analytics Challenges into Solutions
The most common data challenges have straightforward solutions, making data analytics more accurate and actionable.
| Challenge | Implication | Solution |
|
Data Quality |
Poor data quality can lead to inaccurate insights and decision-making. | Implement strong data governance frameworks to ensure accuracy, completeness, and reliability. Use tools like automated data cleaning and validation software. |
|
Integration Across Silos |
Data silos hinder the flow of information across departments, making it difficult to gain insights. | Adopt centralized data platforms, such as data lakes or warehouses, to unify data sources. Encourage cross-departmental collaboration and streamline workflows. |
|
Security |
Increased data usage raises concerns about cybersecurity threats and data breaches. | Invest in advanced cybersecurity measures, including encryption, access controls, and regular audits. Compliance with industry standards like GDPR or HIPAA ensures secure data practices. |
|
Talent Shortage |
There is a significant shortage of skilled professionals in data analytics and management. | Develop talent through upskilling and reskilling programs. Collaborate with external experts or invest in analytics platforms that provide user-friendly interfaces. |
Shape Your Digital Vision Accurately
Turn data analytics into your strategic advantage.
Balanced Combo of Data Analytics and Digital Transformation
You can only expect the new trends in data and analytics to pick up the pace when combined with digital transformation. The boundaries of what’s possible in data-driven digital transformation are already being pushed further, and exciting tendencies are taking the stage.
Real-time analytics
Real-time analytics facilitates instant decision-making while processing and analyzing data as it comes. Industries like retail, healthcare, and finance can respond quickly to any fluctuations they spot. AWS is investing in building proprietary AI chips that offer insight into what’s happening right this minute: how the athlete is running the race or whether an F1 race is likely to make it to the finish line first. Real-time analytics capabilities attract investment across the board.
IoT Data Integration
Organizations will be actively combining data from various IoT devices with their existing systems. Through effective data integration, organizations ensure that the insights derived are both comprehensive and actionable. IoT devices generate vast data which poses storage, processing, and analysis challenges. To address these, organizations invest in scalable infrastructure and adopt advanced analytics tools to handle data efficiently. By integrating IoT into machinery like tractors and combines, John Deere enables real-time data collection. Farmers can accurately monitor soil conditions, weather patterns, and crop health to help them adjust immediately based on the gathered data.
Edge Computing
Edge computing, in combination with 5G, will only get smarter and will enable power-intensive tasks to run even on small devices like smartphones. Large enterprises are predicted to adopt edge computing as part of their IT infrastructure at a rate exceeding 40%.
As generative AI advances, data transmission will keep up with edge computing, enabling more local data processing and reducing the need to send sensitive data to the cloud. Additionally, the synergy of edge computing and cloud architectures will improve application development by combining cloud storage and scaling with fast edge processing.
Ethical AI
AI needs oversight to avoid bias, instill fairness, and have accountability in all data-driven digital transformation ventures.
For instance, large language models (LLMs) should be used with data governance in mind, especially in highly regulated industries like healthcare, life science, and finance. In September 2024, the United States, the UK, the European Union, and other nations signed the first legally binding treaty on AI to govern ethical AI. This agreement emphasizes that AI systems must align with human rights, democracy, and the rule of law.
Established in 2024, the Dataset Providers Alliance (DPA) advocates for ethical AI data licensing. The alliance proposes an opt-in system where data usage requires explicit consent from creators and rights holders, aiming to standardize industry practices and promote fairness.
Quantum Computing
Quantum computing has the potential to reshape data analytics. Its parallel processing, which helps solve complex problems faster than traditional computers do, promises to unlock new dimensions of data analysis and insight generation. On December 9, 2024, Google announced the production of its latest quantum chip, Willow, which has 105 qubits, performs error correction more effectively, and can handle computational tasks far beyond the capabilities of classical supercomputers.
Data-Driven Digital Transformation Has Taken Off
Companies have all sorts of data today, and data analytics is about separating the signal from the noise. If you can navigate data challenges skillfully and support your organization with the benefits data analytics provides, you can ensure business continuity. Remember, digital transformation is a process rather than a destination — the moment you start your business, you set its digital transformation in motion.
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