3 Ways Intelligent Analytics Can Improve Patient Outcomes in Healthcare

Hartford HealthCare, a comprehensive and integrated healthcare system serving more than 17,000 people daily across its 400 locations, recently announced its decision to launch a novel research initiative with Ibex Medical Analytics.

Ibex is the pioneer in AI-powered cancer diagnostics and will use its AI solution ‘the Galen Breast’ to help cancer diagnosis and improve patient care.

This initiative is a natural progression in Hartford HealthCare’s ongoing digital transformation. The AI assistant could provide a greater safety net with minimal effort when pathologist staffing and recruitment are becoming more challenging due to the fast-increasing number of cancer cases. The initiative underlines the growing importance of intelligent analytics in improving diagnostics and patient care.

While everyone agrees on the importance of good health care, medication errors alone cost about US$ 42 billion each year. 4 out of 10 patients suffer during primary and ambulatory healthcare, and often, these errors are related to diagnosis, prescription, and use of medicines. Healthcare experts believe patient engagement is the key to safer healthcare and can reduce the harm and the subsequent cost by up to 15% annually.

The growing concerns in the healthcare industry have put the spotlight on intelligent analytics to help clinicians and caregivers improve patient outcomes.

Intelligent diagnostics rely on Artificial Intelligence to create new pathways for healthcare

Due to its ability to analyze massive amounts of data with efficiency and accuracy, AI plays a transformative role in healthcare. It paves the path for analytics. It empowers healthcare professionals with clinically-relevant insights to diagnose diseases correctly. Artificial Intelligence is now part of the everyday workflows of top healthcare organizations. No wonder AI spending in the healthcare and pharmaceutical industries is predicted to surge.

From US$ 463 million in 2019 to US$ 2 billion in 5 years, the growth is being attributed to the vital role AI has been playing during the pandemic. Companies like Alibaba, YITU, Graphen, and Google DeepMind have been involved in building tools that could detect the virus and analyze the virus to predict its potential protein structure and track its geographical footprint.

Typically, intelligent analytics helps healthcare companies integrate insights derived from medical device data with patients’ health records to improve workflows and gather evidence of clinical outcomes. Data comes from different sources such as clinics, hospitals, medical insurance, medical equipment, and medical research.

When analytics in tandem with Artificial Intelligence, Machine Learning, and the Internet of Things is applied over big data, it provides actionable insights to make smarter decisions, optimize resources, and offer high-quality patient care. It helps caregivers with the required algorithms to create a value framework for all.

In the healthcare industry, intelligent analytics can be:

  • Descriptive – that examines and describes an event that occurred in the past
  • Diagnostic – that looks into the factors that caused the event
  • Predictive – that analyzes trends and historical data to predict such events
  • Prescriptive – that outlines the actions to be taken to prevent such events and attain future goals efficiently

Connecting the dots in healthcare with intelligent analytics

Intelligent analytics works for diverse use cases in healthcare, including early detection, identifying at-risk patients, preventing equipment downtime, and improving patient outcomes. It accelerates decision-making radically since caregivers have access to the necessary information.

Would surgery lead to any complications? If complications arise, would the patient be able to survive them? What are the odds of a patient getting readmitted to the intensive care unit after showing recovery symptoms?

These questions can be daunting, but analytics has all the answers!

While clinicians have been making quick choices in times of uncertainty, intelligent analytics allows them to make informed decisions. It provides predictive algorithms, and organizations are quick to acknowledge its benefits. As per a survey, 42 percent of those who embraced it have seen improvement in patient satisfaction, while 39 percent have seen significant cost savings.

Let’s look at the top applications to understand why intelligent analytics is an absolute must for the healthcare sector.

Early detection of anomalies in scans

The global anomaly detection market is up for massive growth, with its value predicted to touch US$ 8.6 billion by 2026. Also called outlier detection, it points out events and instances that seem suspicious compared to the rest of the data. In healthcare parlance, detecting anomalies early on can help prevent insurance fraud. It allows clinicians to differentiate the normal from the abnormal based on algorithms derived from data.

Especially when different anomalies exist, anomaly detection plays a critical role in ensuring accurate diagnosis.

AI company InformAI is highly focused on healthcare, offering products that improve radiologist productivity. One of the leading medical imaging companies developed AI-enabled image classifiers and patient outcome predictors to speed up medical diagnosis at the point of care. With access to colossal medical datasets, the company comes with key differentiators.

Services facilitating early detection of anomalies offer:

  • Direct access to the best medical experts and proprietary AI data augmentation
  • Model Optimization
  • 3D neural network toolsets

Precision medicine

The precision medicine market is growing, too, as it continues to transform the healthcare business. It involves a thorough examination of patient-specific data that plays a pivotal role in identifying and treating diseases. Even though it is a relatively new strategy, it is expected to touch US$ 278.61 billion by the end of 2030.

It is growing at a CAGR of 11.13% for the forecast period from 2020-to 2030, enabling ground-breaking research and treatments. It investigates a variety of illnesses by analyzing the genetic composition of patients and then creating a customized treatment plan for each patient.

Genoox, the company attempting to build the largest, most diverse real-world evidence dataset to provide answers to any genomic question, is making an impact with the most up-to-date genetic information. It helps healthcare professionals with precision insights to personalize treatments by translating genomic data into accurate, actionable insights at the point of care. It attempts to uncover the unknown with AI-powered technology to offer a real-world genomic evidence dataset.

Services leveraging genetics in precision medicine help:

  • Identify defects in newborns
  • Assess risks associated with inherited diseases
  • Understand drug prescriptions and dosage
  • Offer targeted therapies

Chronic disease management

The chronic disease management (CDM) market is predicted to reach US$ 14,329.15 million by 2029 as CDM continues to assist individuals impacted by a chronic condition with knowledge, resources, and medical care. The growth can be attributed to the prevalence of chronic diseases that necessitate better solutions and services, the increasing geriatric population, and the rise in chronic diseases.

Chronic disease management leverages artificial intelligence to efficiently process data and respond to it. Remedy Health, a leading AI-powered digital platform committed to empowering caregivers and healthcare professionals, offers information and insights to help deliver the best care.

It gives them access to expert opinions and tools and helps them uncover chronic illnesses via phone screening interviews. Early detection is the key to ensuring proper treatment, care, and desired patient outcomes. While competitors depend on sparse medical details provided to them only after the patient is admitted to the hospital, those using this platform capture a vast amount of clinically-relevant data for timely decision-making.

Chronic disease management services allow you to:

  • Offer care throughout the patient journey with actionable health and medical facts, insights, and applications
  • Maintain patient-specific models to determine risks and optimal treatment
  • Personalize treatments for chronic heart problems and life-threatening diseases like cancer and stroke
  • Ensure remote patient monitoring and self-management via support from wearable devices
  • Detect warning signs of illnesses and prevent disease progression with early intervention strategies

AI and analytics are now an integral aspect of health care. They aid in robotic surgeries empowering surgeons with precision and efficiency. These technologies form the core of administrative workflows, bridging the gap between patients and caregivers. AI-powered virtual nursing assistants also play a pivotal role in modern healthcare settings.

The question is – Are you leveraging AI-powered tools and applications well?

Give your business the power of intelligent analytics with Trigent

The stupendous growth in AI and analytics emphasizes the importance of these technologies in navigating through the healthcare landscape. We can help you explore the full potential of analytics and MedTech to offer value to your customers. Our technology experts can guide you to ensure you make the right AI investments, eliminate data silos, and create cutting-edge healthcare solutions.

Allow us to help you overcome data issues and simultaneously leverage advanced technologies to improve care and cure. Call us today for a business consultation.

The Future of Retail Analytics

The importance of retail analytics

The pandemic disruption challenged online and offline retailers on various fronts. Despite the decline in footfall due to restrictions, data indicates shoppers’ preference for an in-store shopping experience.

A PwC survey confirms almost 40% of consumers visit a physical store at least once a week to make purchases. While 65% of shoppers opted for in-store shopping to avoid delivery fees, over 60% chose it to get the items immediately. 61% of consumers prefer in-store shopping because they like to feel or try the products.

While it is not the apocalyptic scenario predicted for brick and mortar retailers, transformation in the experiences is here to stay. 62 percent of Baby Boomers and 58% of Gen Zers1 who prefer in-store shopping would like the conveniences of online as well.

Retailers can make the most of this demand and shift the tide in their favor with retail analytics. Pure-play online players like Alibaba or Amazon owe their success to retail analytics. It helped them understand their customers to come up with hyper-personalization strategies. Physical stores can level up too by implementing data-driven strategies. After all, they have one very distinct advantage over pure-play retailers – physical availability.

No wonder the conversion rate for brick and mortar retailers2 is higher (almost 13 percent) than that of online retailers, which are around 3 percent.

Physical stores are here to stay. Retail analytics paves the way.

There is an abundance of data in the Retail sector across consumer preferences, inventory usage patterns, transaction records, and more. Collating and studying the relevant data with the appropriate Data Analytic Tools is the difference between gaining insight and getting lost in dense details of the business.

For example, Starbucks, the coffee chain giant, used data analytics to empower human connections, enhance in-store experiences, and induce transparency in the supply chain. Analytics helped it brew exciting menus, offers, and addictive coffee for its more than 100 million weekly customers.

Yet, most retailers cannot attain the data maturity required to stay ahead.

Leverage data insights to delight your customers with a magical experience. Contact us now!

While 25% of retailers are leading in terms of their data maturity, over 50% are still struggling to prioritize investment in data capabilities and find the right people on their path to digital transformation.

The in-store experience is the leverage. In-store analytics can make a difference.

Modern retailers are using various methods to track the movement of customers once they step into the store.

Innovations like smart mannequins that analyze faces to determine age, gender, race, and the time spent by shoppers at the stores help brands enhance the in-store experience. Smart carts with location beacons and sensors collect customer data on the store sections visited, time spent, products selected but discarded prior to purchase, and more.

In-store analytics helps:

  • Differentiate between shoppers and consumers and how they behave from the entrance to the exit
  • Identify products that fly off the shelves and those that don’t
  • Prevent theft and shoplifting
  • Evaluate the effectiveness of store displays and employee actions that motivate purchases
  • Efficiently assign staff resources.

The Chinese eCommerce giant Alibaba’s retail store Hema allows customers to scan barcodes through an app to get product information and pay for their groceries. This provides insights into shopper preferences on products not purchased. The analytics enable them to create personalized shopping experiences, devise loyalty programs and targeted promotional campaigns.

Also Read: How the use of technology in retail stores are helping them withstand competition

Analytics is key to optimizing inventory and supply chain logistics

The one thing that matters most for every business is maximizing sales.

Retail analytics helps retailers:

  • Manage inventory and achieve assortment optimization
  • Ensure demand fulfillment by stocking the right product at the right place
  • Align merchandising decisions with customer expectations and performance of product categories
  • Assess new products for their incremental financial contribution and the value they offer to customers
  • Ensure smart delisting to replace slow-moving items with those in demand
  • Enable optimal space utilization based on the popularity of products and their contribution towards overall profitability

Nordstroms’ incident offers important lessons for Retailers. Beyond its premium stores, they relied heavily on Nordstrom Rack discount stores to overcome the pandemic-induced lull. The company added more products at lower price points while adjusting its assortment. Low inventory levels in premium brands across key categories led to understocking, creating a gap in merchandise availability. 

Shares plunged by about 23 percent, and CEO Erik Nordstrom was quick to confess, ” We brought some lower price product in categories that we’ve heard from customers is not what they [want].”

While it is good to look into ‘what could go right’ with data analytics, it is essential to prepare for things that could go wrong effectively. 

Assortment analytics should be supported by inventory analytics to optimize the supply chain. 

The French retailer Carrefour uses AI-powered predictive analytics to optimize inventory management across warehouses, stores, and websites. It helps the company predict demand, refine supply orders, reduce stock outages, and avoid overstocking.  

Demand fulfillment should be effectively balanced against the cost of excess inventory to achieve profitable outcomes. 

BWG, the company behind Spar in Ireland, is all set to roll out an AI-based predictive stock ordering solution. With a €6.5 million investment in smart technology, it will use it to anticipate customer behavior across its 1,000-plus stores. 

Successful retailers rely on:

Descriptive analytics – It gives them insights about the performance of their business actions to tweak marketing campaigns and determine response rates, conversion rates, and costs per lead. It is more effective when used along with web analytics

Diagnostic analytics – It looks at past performance too but it studies the relationship between variables and outcomes. It helps retailers understand ‘why’ they got those outcomes to decide what they can do in the future. 

Predictive analytics – It helps retailers analyze shoppers’ behavior based on insights obtained from diagnostic analytics. It enables them to forecast trends and stock accordingly. 

Prescriptive analytics – It helps retailers make incremental adjustments to match steps with changing sentiments, demand, and supply shocks. Recommendations come in real-time and changes can be made immediately. It’s how airlines adjust their ticket prices.

The Spanish retailer Zara exemplifies what retailers can achieve with retail analytics.

It manages a tight supply chain with real-time updates on SKU-level inventory data. It gives customers what they want while keeping the ones that lack the pull away. 

Zara obtains qualitative feedback from sales employees to understand customer sentiment. It initially orders in small batches and increases the inventory only when the designs get a satisfactory response in store. 

In contrast to its competitors like H&M which creates 80% of the designs ahead of the season, Zara designs only 15-25% ahead of the season and more than 50% mid-season depending on what becomes popular. Its quick refresh cycles create a sense of scarcity that further increases the demand for its designs. 

As Masoud Golsorkhi, the editor of Tank, a London magazine puts it, “With Zara, you know that if you don’t buy it, right then and there, within 11 days the entire stock will change. You buy it now or never. And because the prices are so low, you buy it now.”

Tools for data-driven retail

Data generated, captured, copied, and consumed globally has increased by a whopping 5,000% from 2010 to 2020. 

There’s a lot of raw data out there that can offer a wealth of insights to study customer needs and deliver delightful experiences. All you need is a robust data analytics tool. 

Embark on a retail analytics journey with Trigent

The right data tools can help build a suitable data ecosystem. Our extensive suite of data analytics tools can help you align data investments to desired business outcomes. 

We can help you prioritize data to transform even the most challenging use cases into avenues for growth. Call us today!


  1. https://review42.com/resources/retail-statistics/
  2. https://www.forbes.com/sites/patfitzpatrick/2020/07/23/retail-conversion-rate-secrets-you-never-knew/?sh=68eff76dfbaa

Effective Predictive Maintenance needs strategic automation and human insight

New-age technologies like Artificial Intelligence (AI), Machine Learning (ML), Internet of things (IoT), and predictive analytics are automating processes and augmenting human capabilities. Together, they set the stage for innovations in different sectors. Manufacturing is leveraging Predictive Maintenance (PdM) that takes preventive maintenance several notches higher.

What is Predictive Maintenance?

PdM changes the approach from reactive to proactive maintenance, empowering enterprises to anticipate changes in the system and preemptively manage them. In other words, it helps enterprises predict and avoid machine failure and resultant downtimes. These analytics-led predictions optimize maintenance efforts and facilitate frictionless interdependence.

According to Deloitte, PdM increases equipment uptime by 10-20% and reduces overall maintenance costs and maintenance planning time by 5-10% and 20-50% respectively. With a CAGR of 25.2%, the global predictive maintenance market is set to grow from USD 4.0 billion in 2020 to 12.3 billion by 2025. The growth is fueled by the continued demand for reducing maintenance costs and downtime.

In the current Industry 5.0 environment, the role of maintenance has evolved from merely preventing downtimes of individual assets to predicting failures and creating synchrony between people, processes, and technologies. Predictive maintenance plays its part well, though it does bring along certain challenges that necessitate human intervention.

Benefits of predictive maintenance in manufacturing

As mentioned earlier, predictive maintenance helps eliminate unplanned downtime and related costs. In an IoT-driven world where sensors, devices, systems, etc. are connected, McKinsey believes that the linking of physical and digital worlds could generate up to $11.1 trillion annually in economic value by 2025.

Maximized runtime also means better profits, happier customers, and greater trust. Predictive maintenance can ease logistics by choosing maintenance time slots outside of production hours or at a time when the maintenance personnel is available. It contributes to supply chain resilience, material costs savings, and increased machine lifespan.

However, PdM is only as good as the data it relies upon. Due to IoT technology, data comes from different sources and needs to be duly analyzed before it can be harnessed to make predictions. Hence the importance of IoT Predictive Maintenance

Limitations of predictive maintenance

We need to consider several elements to translate the information PdM provides into positive outcomes. For instance, depending on usage and maintenance history, it may advise you to replace a certain part or component. But this information can lead to further questions. You may need help in deciding which brand and vendor to consider, whether replacement of the component is a good option, or would it make better sense to replace the equipment entirely.

The forecast is often prescriptive and based on statistical models. While optimizing the operational efficiency of a particular line of business, PdM often fails to consider how it impacts other lines. For instance, when it suggests particular equipment is due for maintenance, it may not be able to offer advice as to where the production/processing needs to be shifted when it’s down. The value it offers will therefore be shaped by how decision-makers respond to predictive data.

Data quality and coverage are critical to make predictive maintenance work for the organization. For data to be suitably collected, integrated, interpreted, and transformed, we need dashboards, notification systems, and a bunch of other things to get started. This requires considerable research and planning to go into its implementation for it to start providing the insights we need.

Predictive maintenance use cases in manufacturing – The key lies in the way you respond

Decision-makers typically respond to predictive data with either hypothesis-driven or data-driven responses. The former stems from past business experiences and determines the plan based on a limited scope of response actions. Data-driven responses, on the other hand, aim to find solutions based on real-time business realities and consider several optimization scenarios to determine the way forward.

In contrast to hypothesis-driven decision-making, optimization ensures that all possible paths are explored and evaluated, relevant constraints are taken into consideration, and cross-functional interdependencies are looked into. A workable scenario based on business realities is thus created with no scope for purely intuitive responses.

Despite the analytics-driven insights, predictive maintenance is incomplete without human judgment. Smart decisions come from the ability to visualize the physical and financial outcomes before enforcing them. High-risk situations might arise, and thus they are best left to human discretion.

A predictive maintenance model for Industry 5.0

Manufacturers need clarity on several variables to understand the implications of failure. A false alarm triggered due to inaccurate predictions can result in a lot of unwarranted chaos and anxiety. However, a missed detection might often prove to be a costly error, sometimes resulting in loss of humans and property. Therefore, while understanding variables, they need to first know how often the variable behaviors occur on the factory floor. Strong domain knowledge along with solid data based on previous failures and scenarios is the key to understanding a machine.

Prediction accuracy will improve if we have adequate data on the behavior of machines when they are very close to failure. Only skilled personnel can determine this; some data sets, despite being important, are harder to collect and yet very critical for decision-making.

If we need data on a machine that breaks just once in a year or two, we need to work closely with machine makers who already possess a large pool of relevant data. Alternatively, we may choose to create a digital or a simulation model to create relevant data sets. The most expensive failures are usually the ones we never expect and hence relevant testing for different scenarios should also be considered.

The future of predictive maintenance

The way forward into Industry 5.0 is to create a predictive model that uses analytics, machine learning, and Artificial Intelligence (AI) in conjunction with human insights.

Manufacturers are now relying on predictive models to facilitate smart manufacturing as they struggle with quality issues more often than machine failures. Unusual temperatures, random vibrations, are all telltale signs that a machine may be in dire need of maintenance. Simple data sets can be a good starting point as we scale up with the right predictive maintenance solution. But, in the end, it’s the human insight that can give predictive maintenance its winning streak.

Predict business success with Trigent

At Trigent, we are helping organizations benefit from Industry 5.0 We help them build value with predictive analytics and rise above maintenance challenges. With the right guidance, we help them foster the man-machine symbiosis to harness new levels of operational efficiencies.

Call us today for a consultation. We’d be happy to help with insights, solutions, and the right approach to predict better business outcomes.

Why Advanced Analytics is the Future of Healthcare Organizations

Research and Markets announces that the global market for advanced analytics totalled $207.4 billion in 2015, and should total nearly $219.3 billion by 2020, a five-year compound annual growth rate (CAGR) of 1.1%, through 2020. According to them, the advanced analytics market comprises applications for the following industries: banking and financial services, telecommunications and IT, healthcare, government and defense, transportation and logistics, and consumer goods and retail.

Focusing on the healthcare industry, their larger-than-life problem today is the need to provide value to patients, while remaining cost effective and competitive. They need to move from volume-based services to value based services, by providing more for less and become more patient-centric. But how is this possible in a typical scenario where medical professionals are often overworked due to lack or shortage of staff. Where complex illnesses, longevity and lack of knowledge are contributory factors, upsetting the equilibrium of the industry!

Superimpose this scenario with the Internet era, where patients have more access to information, and their expectations from their healthcare providers is also higher. Where they demand more accountability from doctors, nurses and even their health plans and you know the magnitude of their woes.

If healthcare organizations are able to manage all these problems, they still need to find ways to differentiate themselves from competition to attract and retain people.  Where are the resources, the time and the people to achieve all this in a fast moving scenario?

Moving away from internal issues, healthcare organizations are stressed to differentiate themselves to attract and retain people.

Maybe then analytics can be a solution as it provides better insights into treatments and technologies. It can help to improve efficiencies, reduce risk and provide a means to gather and decipher critical data to provide better services.

Seeing the potential of information technology, there has been a proliferation of clinical research systems, electronic health records and devices since the last five years or so. Information explosion and an abundance of data exists today as a result of these devices, but this is resulting in clutter more than intelligence. It is an added dilemma for health organizations to sift through this information to find real value from the same. Already overworked and understaffed, healthcare organizations find data daunting rather than determining.

Luckily the trend is already changing and analytics in healthcare is paving the way for predictive intelligence where healthcare organizations can use data to make intelligent predictions.

Healthcare analytics is not a destination, but a journey that is never completed. If we were to look at an example of analytics in healthcare, we can say, that retrospective analytics is most common, where a hospital looks at its records to see the number of patients who were admitted, causes for admission and so on and so forth. Predictive analysis would require taking this data and looking for common trends to predict the future and finally optimizing the results to save costs and provide greater value to patients will complete the cycle.

Advanced analytics requires the help of a software company which has deep domain knowledge. While most healthcare companies, due to security and fraudulence fears may believe that managing data is an in house task, the fact remains that it requires in-depth technical and domain knowledge to convert data into intelligence.

Info-graphic on Business Intelligence for Manufacturers

A Picture is worth a thousand words” – I would have been lucky if It had struck my mind before drafting a 1000 + words post. But then I realized why not convey it through an info-graphic. As they say an enlightening idea comes after you had put all your efforts, I seem to experience similar fate. Anyways, all’s well that ends well. So, here’s my info-graphic depicting a typical scenario in Manufacturing.

If you are one among those readers who have plans for BI particularly in Manufacturing sector and using Microsoft Stack, this info-graphic can help you make a sound choice.

BI-infographics for Manufacturing Companies

EXCEL vs BI tools – Towards a Data Driven Culture!

The Great BI vs Excel Debate

As Business intelligence vendors slog it out to flex their muscles on enterprise stage and has experts talking lengths on BI, one thing that is often dropped out is the real protein aka Excel; from where the BI concept got ripped and is now flaunting its curves on enterprise’s Dashboards.
One cannot undermine the fact that, the DNA of “business analytics” which is the core part of business intelligence still remains deeply entrenched in analytical capabilities of Excel (that has long been and still is the mainstay of enterprises’ analytics needs). However, at present, the real power of any tool or application is evaluated on the basis of its ease of use and intuitive features that can help even non tech savvy people get relevant insights as per their needs
without relying too much on IT staff.

The Great BI vs Excel Debate

So, let’s uncover which areas, Excel needs to flex its muscles in its run up to the Business intelligence race.

Types of Users and Familiarity

Proponents of Excel are those power users who can perform almost all Excel analytical stunts. But, when it comes to less tech savvy frontline executives or other personnel, mastering Excel would be a tough pursuit. Here’s where modern BI tools makes it easy for even the non-techie employees perform slicing and dicing, do some data mash-ups with intuitive visualization features.

Timely re-configuration and processing

One of the areas where BI tools scores over Excel is providing real time data which is outside the purview of Excel. The ability to connect directly to the databases and heavy under the hood plumbing makes real time data monitoring easy and actionable. Updating and re-configuring data also consumes a lot of time in Excel and sometimes runs out of memory as well. Due to these inconsistencies working with Excel becomes difficult. The amount of time taken to download data in Excels sheets and tons of manipulation done in order to get relevant insights from disparate applications seems quite a daunting task.

Visualization and Web access

Modern BI tools provide rich interactive visualization, web access to analytics as well as new forms of interactive data visualization which still remains a far cry for Excel.

Error Probability

Mismanagement of data, corrupt files and inadequate security makes Excel more vulnerable to errors. Even, power users who can build their own macros might still run the risk of making hidden errors. Here, BI tools that are integrated with the databases help in master data management and these tools being rigorously tested, come as a saving grace for many users.

Complex Decision Making

When it comes to complex decision making that requires manager to access information from various applications like SAP, CRM, ACCOUNTING etc. and then downloading them to Excel sheets and performing analysis becomes a complex procedure. On the other BI tools are far more sophisticated and can be easily integrated with cross functional applications to provide meaningful insights without banging heads on the Excel walls.

Complex Marketing Research and statistical analysis

In order to perform complex research and statistical analysis, Excels fails to leave a mark as compared to the like of Spss and Sas. These softwares provide domain driven data mash up capabilities and are far easy to use then Excel. Further, managing unstructured data becomes difficult in Excel.


BI tools are more expensive then Excel, this is one area where Excel holds sway over BI tools. For smaller organization Excel can help solve much of these BI challenges in cost effective way, when it comes to big organization, Excel doesn’t stand a chance against mighty BI providers like COGNOS, QLIKVIEW, TABLEAUE and the like.

Size of data

Though Microsoft has made significant changes in Excel to enable power users to manage large amount of data, Excel still falls short of space while handling large amount of data. This is one major problem with Excel comparatively with BI tools that come with high storage capacity to handle large data sets.

Computed measures KPIs, Etc.

Though Excel can perform complex analysis, when it comes to performing analysis to measure KPIs using disparate data sources, BI tools are miles ahead.


BI tools offer better compliance management capabilities than Excel. BI tools provide much more reliable data for auditing various standards where quantification of process and information flow is reliable and streamlined.

Operational BI at Banks

Embedding Operational Business Intelligence (BI) tools to several banking operations can help operational managers get actionable insights on operational bottleneck, historical data etc. for managing, monitoring and for controlling contingencies.

By getting actionable and real time  insights on operational bottlenecks, managers can take corrective measures to eliminate operational flaws.  Let’s consider a simple example of a cheque clearance process at banks and see how Operational BI can help you get valuable insights to improve functional inefficiencies.

Operation BI for Banking and financial operations

‘A’ deposits a cheque in favor of ‘B’s account on day one. Afterwards, B’s bank processes the cheque which includes validation of cheque’s credentials and passes it on to A’s Bank. The cheque validation processes include a series of steps to identify instances of frauds, validation of  the sufficient amount of money at A’s account,  signatures,credentials, etc. and other issues that may obstruct the fund’s transfer process. The settlement process is over when A’s bank clears the payment to B’s Bank.

The cheque clearance process might seem simple at first sight, but there are several intricacies involved within the process. Firstly, there are many instances of frauds and tamperings such as counterfeit, forgery or alterations. Secondly, there are unpaid instances such as insufficient funds at customers account or there are several fields wrongly entered such as dates, wrong signature etc. It would be far too simple to manage these cases if there were less number of transactions, but consider how difficult it would be if there are millions of cheques being deposited and cleared every day. Besides, it is difficult to give answers to customers who inquire about why their cheque’s being bounced or other queries related to their settlement processes.

Where does Operational BI fits in?

Since the settlement process is complex and takes a lot of time in terms of the number of days to clear a cheque, wouldn’t it be nice if fraud cases could be detected real time to reduce operational risks?  Well, here’s a tip on how you can do this? Business rules can be applied by analyzing history of frauds that would signify potential fraud while managing daily operations and reducing the number of frauds. Operation BI can also help customers get real-time information about their queries thereby reducing user reaction for any issue. It can also be integrated with functional processes and functional applications to get actionable insights. In the cheque clearing process, an operational BI can help managers know how many cases of frauds appeared per day, cases of insufficient amount, cases of halt, or number of cheques that were forged or tampered.

Interesting Video by Logi Analytics themed around The BI Chocolate Cake Problem”.”

Here is a very interesting video by LOGI analytics. The video metaphorically describes enterprises’ end-users as kids and toddlers demanding for variety of chocolate cakes (figuratively representing data reports) and their increasing reliance on IT staff to get personalized reports on time. It also features IT staff’s pathos on how they are flooded with the demands for variety of cakes(aka reports) and their inability to provide reports on time apart from focusing on their workday routine jobs.

So watch out for yourself before I spill the beans in this piece.

Click on the Image To Play

‘The BI Chocolate Cake Problem’
Exit mobile version