Business Intelligence And Analytics Quotes

Tweet others the way you want to be tweeted.

We have met the enemy and he is us.” We need to change the ways we do our job.

An intelligent organization is not about the “cleverness” of one analytics team but the insightful nature of the entire business.

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Simon Leigh Pure Reputation, The Role of AI in Online Reputation Management (ORM) Artificial Intelligence (AI) plays a transformative role in Online Reputation Management by automating monitoring, analysis, and response to online content. AI-powered tools scan social media, review platforms, forums, and news sites in real time to detect mentions of a brand or individual. Through sentiment analysis, AI evaluates whether the mentions are positive, negative, or neutral, helping businesses gauge public perception instantly. AI also enables predictive analytics, identifying emerging reputation risks before they escalate. Chatbots and automated response systems use natural language processing (NLP) to manage customer interactions quickly and consistently. Additionally, AI supports content generation and SEO optimization, ensuring positive brand stories and authoritative profiles rank higher in search results. Overall, AI enhances ORM by making it faster, data-driven, and proactive, allowing organizations to protect and strengthen their digital reputation efficiently.

Emotional intelligence is born largely in the neurotransmitters of the brain’s limbic system, which governs feelings, impulses, and drives. Research indicates that the limbic system learns best through motivation, extended practice, and feedback. Compare this with the kind of learning that goes on in the neocortex, which governs analytical and technical ability. The neocortex grasps concepts and logic.

It is difficult to evaluate their recommendations of individual securities. Each service is entitled to be judged separately, and the verdict could properly be based only on an elaborate and inclusive study covering many years. In our own experience we have noted among them a pervasive attitude which we think tends to impair what could otherwise be more useful advisory work. This is their general view that a stock should be bought if the near-term prospects of the business are favorable and should be sold if these are unfavorable—regardless of the current price. Such a superficial principle often prevents the services from doing the sound analytical job of which their staffs are capable—namely, to ascertain whether a given stock appears over- or undervalued at the current price in the light of its indicated long-term future earning power. The intelligent investor will not do his buying and selling solely on the basis of recommendations received from a financial service. Once this point is established, the role of the financial service then becomes the useful one of supplying information and offering suggestions.

Businesses should free themselves from dogma, especially when leveraging data to build a business. No one got very far living out other people’s thinking.

4. The potential levers to improve employees’ experience We have identified three levers to enable the transition from the current breakdown of employee activities to the ideal division of activities. They are: Automate: companies should identify and automate routine activities, such as generating a PowerPoint presentation for a weekly meeting or recording invoices in accounting software. Augment: organizations should seize the opportunity to increase the value of work activities delivered by employees. IA is used as a crucial component here, with, for example, the generation of insights through advanced analytics to help decision making. Abandon: some work activities do not fit with leading practices for efficient work, and represent an obstacle to the employee’s experience. These activities should be reduced or eliminated. For example, restricting the volume of meetings and email traffic is essential. We call these levers the “Triple-A artifact”. It has proven to be a handy framework to help organizations build their action plans to boost their employee experience.

Management becomes more complex, too, because the staff has to reach across the organization at a level and consistency you never had to before: different departments, groups, and business units. For example, analytics and business intelligence teams never had to have the sheer levels of interaction with IT or engineering. The IT organization never had to explain the data format to the operations team. From both the technical and the management perspectives, teams didn’t have to work together before with as high of a bandwidth connection. There may have been some level of coordination before, but not this high. Other organizations face the complexity of data as a product instead of software or APIs as the product. They’ve never had to promote or evangelize the data available in the organization. With data pipelines, the data teams may not even know or control who has access to the data products. Some teams are very siloed. With small data, they’ve been able to get by. There wasn’t ever the need to reach out, coordinate, or cooperate. Trying to work with these maverick teams can be a challenge unto itself. This is really where management is more complicated.

How can you run Analytics “as one”? If you leave Analytics to IT, you will end up with a first-class race car without a driver: All the technology would be there, but hardly anybody could apply it to real-world questions. Where Analytics is left to Business, however, you’d probably see various functional silos develop, especially in larger organizations. I have never seen a self-organized, cross-functional Analytics approach take shape successfully in such an organization. Instead, you can expect each Analytics silo to develop independently. They will have experts familiar with their business area, which allows for the right questions to be asked. On the other hand, the technical solutions will probably be second class as the functional Analytics department will mostly lack the critical mass to mimic an organization’s entire IT intelligence. Furthermore, a lot of business topics will be addressed several times in parallel, as those Analytics silos may not talk to each other. You see this frequently in organizations that are too big for one central management team. They subdivide management either into functional groups or geographical groups. Federation is generally seen as an organizational necessity. It is well known that it does not make sense to regularly gather dozens of managers around the same table: You’d quickly see a small group discussing topics that are specific to a business function or a country organization, while the rest would get bored. A federated approach in Analytics, however, comes with risks. The list of disadvantages reaches from duplicate work to inconsistent interpretation of data. You can avoid these disadvantages by designing a central Data Analytics entity as part of your Data Office at an early stage, to create a common basis across all of these areas. As you can imagine, such a design requires authority, as it would ask functional silos to give up part of their autonomy. That is why it is worthwhile creating a story around this for your organization’s Management Board. You’d describe the current setup, the behavior it fosters, and the consequences including their financial impact. Then you’d present a governance structure that would address the situation and make the organization “future-proof.” Typical aspects of such a proposal would be The role of IT as the entity with a monopoly for technology and with the obligation to consider the Analytics teams of the business functions as their customers The necessity for common data standards across all of those silos, including their responsibility within the Data Office Central coordination of data knowledge management, including training, sharing of experience, joint cross-silo expert groups, and projects Organization-wide, business-driven priorities in Data Analytics Collaboration bodies to bring all silos together on all management levels

with everything you need to know about the ins and outs of data mining. This book has been laid out in straightforward and clear chapters with each chapter focusing on a particular part of data science for business to be able to ensure that you gain the maximum amount to knowledge without having to weed through unnecessary information. I hope this book answers any question you have and leaves you feeling confident on the subject of data science, data analytics and business intelligence. Chapter 1 Wholeness of Data Analytics There is a lot of data that comes rushing towards an organization of any type and sometimes it can be hard to decipher just what it means to the team and how they can use it to benefit them. This is where data analytics is the more helpful. The data is analyzed through a process of inspecting, cleaning, transforming and modeling that makes the information easier to look at and read. By narrowing down the amount of information, an organization is looking at they are going to be better able to utilize the relevant information and use the conclusions the data suggests to make decisions that are most likely to bring rewards. Although data analytics are most frequently used in business to consumer applications, there are many different facets of the data analysis. Some of the most common places data analytics are utilized in the worlds of business, science, and social science, in a variety of ways. Regardless of the type of organization, you are involved with, and even in your personal life, there are ways to make data analysis work for you. An example of where data analytics would be used in regards to a social networking site. A social networking website collects the information which relates to user preferences as well as the community interested and can segment according to the criteria that have been specified

Business intelligence (BI) is an umbrella term that includes a variety of IT applications that are used to analyze an organization’s data and communicate the information to relevant users.

Emotional intelligence is born largely in the neurotransmitters of the brain’s limbic system, which governs feelings, impulses, and drives. Research indicates that the limbic system learns best through motivation, extended practice, and feedback. Compare this with the kind of learning that goes on in the neocortex, which governs analytical and technical ability. The neocortex grasps concepts and logic. It is the part of the brain that figures out how to use a computer or make a sales call by reading a book. Not surprisingly—but mistakenly—it is also the part of the brain targeted by most training programs aimed at enhancing emotional intelligence.

Health analytics follows a kind of 80/20 rule where 80% of the effort is expended on obtaining, cleaning, warehousing, and tabulating the data.

The greatest unmet challenge in health production is individual-driven behavior change.

they are chasing elusive perfection, the clock is ticking on providing value to the business.

multi-sector collaboration and respects the engagement and inclusion of all partners. It does not practice “top down”, but “across

vast majority of physicians agree that unmet social needs are a direct cause of poor health.

over-treatment accounts for about 30 percent of healthcare expenditures.

All too often the practice of analytics provides answers without knowing the questions.

It is not about the data and its mechanics; it is about solving problems and making wise choices

it starts with the questions: What is the need/challenge, why is it important, and what are the pathways to make things better?

There are seven analytics adaptations derived from successes in other industries that address population health,

it’s not the technology, it’s the sociology”.

the running of the business has not been as successful.

Medical care is a small contributor to health.

a business must mature through a series of stages before it can reach its full potential through analytics.

a total rethinking of IT capability and functionality. And this can be very expensive and take years

a “burning platform” to address the money concerns has driven people together like nothing else before.

There is much more to be learned from a person’s head than from their data streams.

the technology “hows” are ahead of the business “whys” and “whats.

major challenges, including persistent resistance to performance measurement at a granular level

leadership that clearly articulates organizational mission and goals,

Their mantra is “don’t take my spreadsheets away,

behavioral and social factors is six times more important than the delivery of healthcare

>Behavioral economics.

>Health social networks to democratize the data, make meaning of it, and keep people engaged.

digital hug is not as fulfilling as a real one. But, for a certain group of people, it offers a channel out of loneliness and opens the opportunity

there are special peculiarities of the business of healthcare,

there is enough unused healthcare industry data to keep IT busy for decades.

The purpose of PHA is “health intelligence,

>More Modification, Less Medication:

The great untapped potential for health and healthcare is to understand what a customer wants and needs and to respond in ways that lead to improvements in health and well-being.

a whole series of implementation linkages needs to unfold for the analytics investment to pay off.

Ideas need to be converted into a theory of action on how it will accomplish a goal.

Diffusion is essentially a social process through which people talking to people spread an innovation.

other industries can teach healthcare a lot through analytics

healthcare can learn a lot from sports,

As a result, the most important recommendation for organizations of all shapes and sizes moving forward is to anticipate worst case scenarios at a minimum. Even in cases where organizations cannot or will not make some of the operational changes recommended below, the exercise of focusing on nonsoftware areas of a given business can help identify under-realized or -appreciated assets within an organization. Particularly ones for whom the sale of software has been low effort, brainstorming about other potential revenue opportunities is unlikely to be time wasted. One vendor in the business intelligence and analytics space has privately acknowledged doing just this; based on current research and projecting current trends forward, it is in the process of building out a 10-year plan over which it assumes that the upfront licensing model will gradually approach zero revenue. In its place, the vendor plans to build out subscription and data-based revenue streams. Even if the plan ultimately proves to be unnecessary, the exercise has been enormously useful internally for the insight gained into its business.

The yearly survey is essential for strategic decision support, as it shows the overall strengths and weaknesses of the company, and overall customer satisfaction scores are often used as a strategic target.

need‐based segmentation is to understand the needs of customers and why they choose a company, whereas value‐based segmentation has to do with which specific customers a company must focus on to retain their earnings.

If a business, for instance, has built its market position on being the cheapest, it stands to reason that intense focus will be on the optimization of internal processes.

One other problem is that too many people—and vendors in particular—are already using big data to mean any use of analytics, or in extreme cases even reporting and conventional business intelligence.

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

There are five ways technology can boost marketing practices: Make more informed decisions based on big data. The greatest side product of digitalization is big data. In the digital context, every customer touchpoint—transaction, call center inquiry, and email exchange—is recorded. Moreover, customers leave footprints every time they browse the Internet and post something on social media. Privacy concerns aside, those are mountains of insights to extract. With such a rich source of information, marketers can now profile the customers at a granular and individual level, allowing one-to-one marketing at scale. Predict outcomes of marketing strategies and tactics. No marketing investment is a sure bet. But the idea of calculating the return on every marketing action makes marketing more accountable. With artificial intelligence–powered analytics, it is now possible for marketers to predict the outcome before launching new products or releasing new campaigns. The predictive model aims to discover patterns from previous marketing endeavors and understand what works, and based on the learning, recommend the optimized design for future campaigns. It allows marketers to stay ahead of the curve without jeopardizing the brands from possible failures. Bring the contextual digital experience to the physical world. The tracking of Internet users enables digital marketers to provide highly contextual experiences, such as personalized landing pages, relevant ads, and custom-made content. It gives digital-native companies a significant advantage over their brick-and-mortar counterparts. Today, the connected devices and sensors—the Internet of Things—empowers businesses to bring contextual touchpoints to the physical space, leveling the playing field while facilitating seamless omnichannel experience. Sensors enable marketers to identify who is coming to the stores and provide personalized treatment. Augment frontline marketers’ capacity to deliver value. Instead of being drawn into the machine-versus-human debate, marketers can focus on building an optimized symbiosis between themselves and digital technologies. AI, along with NLP, can improve the productivity of customer-facing operations by taking over lower-value tasks and empowering frontline personnel to tailor their approach. Chatbots can handle simple, high-volume conversations with an instant response. AR and VR help companies deliver engaging products with minimum human involvement. Thus, frontline marketers can concentrate on delivering highly coveted social interactions only when they need to. Speed up marketing execution. The preferences of always-on customers constantly change, putting pressure on businesses to profit from a shorter window of opportunity. To cope with such a challenge, companies can draw inspiration from the agile practices of lean startups. These startups rely heavily on technology to perform rapid market experiments and real-time validation.

When it comes to developing and maintaining a competitive advantage for your business, there is no question in my mind that you’re going to need to incorporate both data science and business intelligence together in order to survive the future hyper-competitive environment.  If you are not doing so, I can guarantee that your competitors will be doing it.

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Business intelligence is a broad set of Information Technology (IT) solutions that includes tools for gathering, analyzing, and reporting information to the users about performance of the organization and its environment.

For eight consecutive years, Microsoft has been positioned as a Leader in the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms.

Creating something from nothing is ingenious. Creating something from something is smart. To rule your world, be ingeniously smart.

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