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The challenge was accessing and using it. Your data is spread across a number of different sources When a business draws its data from a range of different sources, it can be difficult to consolidate this information into actionable insights.

Different departments will undoubtedly have varying interpretations on what performance metrics are most important and may use different reporting tools to communicate this data. This internal conflict can severely diminish data quality and make it impossible to form a coherent overview of company performance. You rely on spreadsheets to store your information Spreadsheets may have underpinned the business intelligence operations of SMBs for many years but, as data requirements have grown, they have become an increasingly inefficient way of storing information.

Spreadsheets require a large amount of manual data entry to maintain and this time-consuming approach can cause significant delays, leading to data being both inaccurate and outdated. A BI solution can help to streamline the data collection, analysis and reporting process, removing this dependency on spreadsheets. You have bottlenecks in your reporting In many companies, Business Intelligence and reporting fall entirely under the responsibilities of the IT department.

Business Intelligence software often requires a great amount of technical expertise and non-technical users are therefore heavily reliant on IT staff to manage data and create reports. This can create bottlenecks in the reporting process and these delays can lead to the information being outdated. This dependency also takes time away from the other responsibilities of the IT department and can lead to it neglecting these duties. A BI solution can help to reduce these bottlenecks by giving more power to the people through the ability to self-serve and create their own reports without the need for technical knowledge or experience.

Your data does not provide actionable insights Businesses can often get so caught up in the process of collecting data that they fail to take into consideration the importance of producing actionable insights.

Companies should take a quality over quantity approach to data instead of simply collecting large volumes of meaningless information. Your data is outdated One of the major issues facing businesses is that their data is not up-to-date, meaning that key decision-makers do not have relevant information on which to base their decisions. This could be down to delays in the collection, analysis or reporting of information and can be detrimental for company performance.

A recent study found that only 13 per cent of business leaders say their reports are reliably up-to-date, highlighting just how significant this problem is for companies of all sizes. A BI solution can automate the processing and delivery of data, with many tools offering the ability to schedule reports in advance, ensuring business leaders always have the information they require. You struggle with data visualisation Data visualisation is a key part of Business Intelligence but, without the existence of a BI solution, it often gets neglected.

There is little point in collecting large volumes of data for it then to be poorly presented to the people who matter. Data visualisation tools can help bring your data to life and make it much easier to interpret through the use of charts, visualisations and dashboards. You are unable to access your data on multiple devices In the modern age, it is becoming increasingly important that information can be accessed anywhere, at any time.

Traditional on-site Business Intelligence operations may be seen as a more secure option but they severely restrict the way that data can be accessed and this can limit its utility. Developments in technology such as Cloud Business Intelligence have meant that this data can now be accessed anywhere in the world with users simply needing access to the internet. Advances in Mobile BI also mean that users can access their data on the go through smartphones and tablet devices.

The benefits of this were highlighted in a recent study which found that Mobile BI users saw an increase of 45 per cent in their ability to make business-critical decisions in the allocated time frame. Tick all of the boxes that apply to your business. It might be spread across different systems making it difficult to access. Or it might be raw data which needs processing in order to be useful, making it difficult for you to get at the information you want quickly.

You find out a key product line is selling fast, several weeks after it started to happen. You get management information monthly or quarterly, not weekly or daily. Such people quickly get overloaded, and delays occur. It might be spread across several ERP systems, for instance, or held in different best-of-breed functional systems, or even held in spreadsheets. You know the sort of thing: downloading data into Excel, manipulating it, and then emailing it out.

This is costly and error prone, and those people could be better employed spending time managing, or selling, or servicing customers. However you still might benefit from some of the fantastic benefits of a BI solution.

A Business Intelligence solution is definitely something you should be thinking about right now. However, choosing the right Business Intelligence solution for you can often be an arduous task. The trouble is, attempts to compare and contrast them often leave you more confused than when you started.

What ought to be a straightforward comparison of like-with- like Business Intelligence systems quickly turns into a debate about the nature of Business Intelligence itself. At Matillion, we approach the problem differently. When investing in new software, it is natural and important for this to be the case. We have compiled a list of some of the most important Business Intelligence questions your company should consider. Who is driving the BI project?

When developing a BI strategy, it is important to know who is driving the project, the person who is ultimately responsible for decision making. It is of vital importance that any project is sponsored by somebody high up in the company who has the authority to drive ideas into action.

Is the strategy business- or IT-lead? When it comes to implementing new tools, business users are typically concerned with factors such as speed, clarity of information, ease of use and its sustainability over time. In theory, this would allow much greater collaboration on projects and allow the business to derive the greatest value possible from their BI strategy. However, in reality it is unlikely that this collaboration will be a smooth process and, with such differing views, it is possible that this may divert attention away from the best interests of the business as a whole.

Who are the users? Perhaps even more important than the architects of a BI strategy are the end users of the tools. It is important that your BI strategy is formulated with these users in mind and in order to do this you must gain an insight into who exactly they are.

Daniel Dann describes the three broad classifications of Business Intelligence users as being; strategic, tactical and operational. It is also important to accommodate the different levels of experience that different users may have as well as their level of IT competence. What are your Key Performance Indicators? As part of your BI strategy, it is crucial that you determine what information is most important for your business.

These KPIs should be high-level, well-defined, quantifiable measurements based on pre-established criteria and provide a framework for comparing performance against business objectives. It is crucial that these KPIs are clearly defined and that their use is consistent throughout the whole business in order to avoid any confusion or misinterpretation that may occur with the use of overly technical jargon.

How good is your data? One of the major obstacles facing businesses when developing a BI strategy is the quality of data. One of the major difficulties that businesses face is being able to collect and process such vast amounts of information and this is where BI tools can help.

BI tools can automate the collection of data and process it into clear and actionable insights. This means that you can spend less time collecting the data and more time using it. How secure is your data? One of the biggest concerns that business have about their BI strategy is the security of their company data.

This is one of the primary reasons why businesses have traditionally preferred on-premise Business Intelligence as opposed to outsourcing these operations.

However, technologies such as Cloud BI are becoming increasingly secure and therefore offer a viable alternative to on-premise. Another major factor in a BI strategy is determining how it will be used and on what platform users will view the information.

Cloud BI allows users to access information at anytime, anywhere in the world, overcoming many of the restrictions of traditional BI tools. Mobile BI can allow users to access this information on the go on a range of smartphones and tablet devices. How much does it cost? Cost can be one of the major factors to consider when developing a BI strategy, particularly for SMBs who do not have the vast financial resources of larger businesses.

With all of the new tools and technologies that are available right now, it could be easy to get carried away and make large financial outlays that the company simply cannot afford in the long run. Cloud BI tools can offer a great solution at a fraction of the price of traditional on-premise operations. Business Intelligence solutions can often take a long time to implement, leaving businesses without this vital information for long periods of time.

It is important to factor this implementation period into your BI strategy and be realistic about time constraints and any potential delays that may occur. Again, Cloud BI offers a much more efficient solution to tradition BI because the implementation time is drastically reduced. A traditional Business Intelligence tool typically takes between months to implement whereas, here at Matillion, our customers can be up and running in as little as four weeks.

Some features will matter more to some businesses than others; some businesses may have little need for a particular feature, while others might rely on it heavily. On the next page we will go on to explore eight of these features in greater detail. By making information easily digestible to everyone, data visualisation can foster better decision making processes.

They combine information from a variety of sources, and then present it graphically, providing at-a-glance information on progress against goals and objectives. In short, if your finance staff love Excel, why fight against the tide?

In the wrong hands, information about which customers, or which products, are — for instance — the most profitable or fastest-growing can be highly damaging. To make things easier, we have broken them down into the six most popular categories available. Each has arguments for and against. For: Very scalable, even to the very largest businesses.

Every feature you could ever want and more. Tableau and Qlikview are examples of successful products in this area. Creating new reports is not an end user activity. Crystal Reports — now sold by SAP — is an example of a report-writing tool. Against: It can take a long, long time to implement — and even longer get to real ROI. And, once staffing time is taken into account, the true cost is usually higher than a Business Intelligence solution.

There is no need to worry about integration costs or compatibility. Increasingly, there are web-based solutions which companies can buy into on a software as a service SaaS basis, and access securely over the internet. Matillion belongs to this category. Typically, it is designed to be easy to use and no hardware or software is required. Web-based, so accessible from anywhere. Usually faster to implement than traditional alternatives. Against: Some Cloud-based Business Intelligence systems are just that, namely tools that happen to be in the Cloud, and which may still require hard work such as data integration.

However, at Matillion we believe that Cloud BI is in a league of its own when it comes to the potential return on your investment. By providing the same mix of analytics, reports and dashboards that traditional Business Intelligence provides, but providing them faster, providing them with greater flexibility, and providing them at a lower cost.

In other words, Cloud BI delivers more — and delivers it more cheaply. Despite the benefits that Cloud BI can offer, many businesses still remain sceptical about its implementation. In this section, we aim to tackle this scepticism by assessing some of the most common concerns we have comes across.

This done, we will go on to further explore the fantastic benefits that Cloud BI can offer for businesses of all sizes. By providing the same mix of analytics, reports, and dashboards that traditional Business Intelligence provides, but providing them faster, providing them with greater flexibility, and providing them at a lower cost. In other words, Cloud BI delivers more, and delivers it more cheaply. In this section we aim to tackle this scepticism by assessing some of the most common concerns we have comes across.

Once we have done this we will go on to further explore the fantastic benefits that Cloud BI can offer for businesses of all sizes. The relationship between the Cloud and Business Intelligence has continuously grown in strength over recent years, and yet its use has mainly been restricted to only partial areas of intelligence operations.

A number of underlying concerns have deterred many businesses from rolling out a full-scale Cloud BI project. Yet these concerns are often unwarranted as the Cloud begins to offer an increasingly viable alternative in the move away from on-premise operations. We tackle these concerns head on by busting seven common myths about Cloud BI.

Myth 1: Cloud BI is not secure Security concerns have been one of the main barriers to Cloud adoption in the last few years but we have found that these concerns are decreasing as people become more aware of the truth.

After a general introduction to the business intelligence BI process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data.

Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9.

Throughout the book, mostly open source tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided.

Wilfried Grossmann is retired full professor for statistics at the Faculty of Informatics, University of Vienna. He has published in the areas of business informatics, medical informatics, data mining, operations research, applied statistics and statistical computing. His research focuses on the interface between applied statistics, statistical data management and metadata for statistical information systems.

Business intelligence software platforms need to en- sure a secure encrypted keychain for storage of credentials. Administrative control of password policies should allow creation of security profiles for each user and seamless integration with cen- tralized security directories to reduce administration and maintenance of users. Real time means near to zero latency and access to information whenever it is required.

The result is real-time business intelligence. Business transactions as they occur are fed to a real-time BI system that maintains the current state of the enterprise. The RTBI system not only supports the classic strategic functions of data warehousing for deriving information and knowledge from past enterprise activity, but it also provides real-time tactical support to drive enterprise actions that react immediately to events as they occur.

As such, it replaces both the clas- sic data warehouse and the enterprise application integration EAI functions. Such event-driven processing is a basic tenet of real-time business intelligence. While traditional BI presents historical data for manual analysis, RTBI compares current business events with historical patterns to detect problems or opportunities au- tomatically.

RTBI is an approach in which up-to-a-minute data is analyzed, either directly from Operational sources or feeding business transactions into a real time data warehouse and Business Intelligence system. RTBI analyzes real time data. Real-time business intelligence makes sense for some applications but not for others — a fact that organizations need to take into account as they consider investments in real-time BI tools.

Key to deciding whether a real-time BI strategy would pay dividends is understanding the needs of the business and determining whether end users require immediate access to data for analytical pur- poses, or if something less than real time is fast enough. This technology is real-time business intelli- gence. Latency All real-time business intelligence systems have some latency, but the goal is to minimize the time from the business event happening to a corrective action or notification being initiated.

Some commentators have introduced the concept of right time business intelligence which propos- es that information should be delivered just before it is required, and not necessarily in real-time. Architectures Event-based Real-time Business Intelligence systems are event driven, and may use Complex Event Processing, Event Stream Processing and Mashup web application hybrid techniques to enable events to be analysed without being first transformed and stored in a database.

These in- memory techniques have the advantage that high rates of events can be monitored, and since data does not have to be written into databases data latency can be reduced to milliseconds. Data Warehouse An alternative approach to event driven architectures is to increase the refresh cycle of an existing data warehouse to update the data more frequently. These real-time data warehouse systems can achieve near real-time update of data, where the data latency typically is in the range from minutes to hours.

The analysis of the data is still usually manual, so the total latency is significantly differ- ent from event driven architectural approaches. Because live data is ac- cessed directly by server-less means, it provides the potential for zero-latency, real-time data in the truest sense. Technologies that Support Real-time Analytics Technologies that can be supported to enable real-time business intelligence are data visualiza- tion, data federation, enterprise information integration, enterprise application integration and service oriented architecture.

Complex event processing tools can be used to analyze data streams in real time and either trigger automated actions or alert workers to patterns and trends. Data Warehouse Appliance Data warehouse appliance is a combination of hardware and software product which was designed exclusively for analytical processing.

In data warehouse implementation, tasks that involve tuning, adding or editing structure around the data, data migration from other databases, reconciliation of data are done by DBA.

Another task for DBA was to make the database to perform well for large sets of users. Whereas with data warehouse appliances, it is the vendor responsibility of the physical design and tuning the software as per hardware requirements. Data warehouse appliance package comes with its own operating system, storage, DBMS, software, and required hardware. If required data warehouse appliances can be easily integrated with other tools. MBI is a package that uses existing BI applications so people can use on their mobile phone and make informed decision in real time.

For an example railroad network. Depending on the results provided by the real-time analytics, dispatcher can make a decision on what kind of train he can dispatch on the track depending on the train traffic and com- modities shipped.

Hoboken, N. Ambient Light Publishing. Ross, Peter Weill, David C. Robertson Enterprise Architecture As Strategy, p. APRO Software. Retrieved 16 May Computer World. Ar- chived from the original PDF on 28 May Retrieved 1 April Business Analytics Business analytics BA refers to the skills, technologies, practices for continuous iterative explo- ration and investigation of past business performance to gain insight and drive business planning.

Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods.

In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.

Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to man- agement science. Analytics may be used as input for human decisions or may drive fully automated de- cisions.

Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next that is, predict , what is the best that can happen that is, optimize.

Examples of Application Banks, such as Capital One, use data analysis or analytics, as it is also called in the business set- ting , to differentiate among customers based on credit risk, usage and other characteristics and then to match customer characteristics with appropriate product offerings.

A telecoms company that pursues efficient call centre usage over customer service may save money. Henry Ford measured the time of each component in his newly established assembly line. But analytics began to command more attention in the late s when computers were used in decision support systems. Since then, analytics have changed and formed with the development of enterprise resource planning ERP systems, data warehous- es, and a large number of other software tools and processes.

In later years the business analytics have exploded with the introduction to computers. This change has brought analytics to a whole new level and has made the possibilities endless. Ford himself.

Challenges Business analytics depends on sufficient volumes of high quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available. Previously, analytics was considered a type of after-the-fact method of forecasting consumer be- havior by examining the number of units sold in the last quarter or the last year.

This type of data warehousing required a lot more storage space than it did speed. Now business analytics is becom- ing a tool that can influence the outcome of customer interactions.

When a specific customer type is considering a purchase, an analytics-enabled enterprise can modify the sales pitch to appeal to that consumer. This means the storage space for all that data must react extremely fast to provide the necessary data in real-time.

Competing on Analytics Thomas Davenport, professor of information technology and management at Babson College ar- gues that businesses can optimize a distinct business capability via analytics and thus better com- pete. Es- pecially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.

Analytics often favors data visualization to communicate insight. Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive an- alytics, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics.

Analytics vs. Analysis Analytics is multidisciplinary. There is extensive use of mathematics and statistics, the use of de- scriptive techniques and predictive models to gain valuable knowledge from data—data analysis. The insights from data are used to recommend action or to guide decision making rooted in busi- ness context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology.

There is a pronounced tendency to use the term analytics in business settings e. There is an increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks to do predictive modeling. Examples Marketing Optimization Marketing has evolved from a creative process into a highly data-driven process.

Marketing orga- nizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.

Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization.

Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify IP address, and track activities of the visitor. With this information, a marketer can improve marketing cam- paigns, website creative content, and information architecture.

Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization and customer analytics e. Web ana- lytics and optimization of web sites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. A focus on digital media has slightly changed the vocabulary so that marketing mix modeling is commonly referred to as attribution modeling in the digital or marketing mix modeling context.

These tools and techniques support both strategic marketing decisions such as how much overall to spend on marketing, how to allocate budgets across a portfolio of brands and the marketing mix and more tactical campaign support, in terms of targeting the best potential customer with the optimal message in the most cost effective medium at the ideal time. Portfolio Analytics A common application of business analytics is portfolio analysis.

In this, a bank or lending agency has a collection of accounts of varying value and risk. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole. The least risk loan may be to the very wealthy, but there are a very limited number of wealthy peo- ple. On the other hand, there are many poor that can be lent to, but at greater risk.

Some balance must be struck that maximizes return and minimizes risk. The analytics solution may combine time series analysis with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a port- folio segment to cover any losses among members in that segment. Risk Analytics Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers.

Furthermore, risk analyses are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions like Online Payment Gateway companies to analyse if a transaction was genuine or fraud. For this purpose they use the transaction history of the customer. This helps in reducing loss due to such circumstances. Digital Analytics Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, pre- dictions, and automations.

This also includes the SEO Search Engine Optimization where the keyword search is tracked and that data is used for marketing purposes. Even banner ads and clicks come under digital analytics. All marketing firms rely on digital analytics for their digital marketing assignments, where MROI Marketing Return on Investment is important. Security Analytics Security analytics refers to information technology IT solutions that gather and analyze security events to bring situational awareness and enable IT staff to understand and analyze events that pose the greatest risk.

Solutions in this area include security information and event management solutions and user behavior analytics solutions. Software Analytics Software analytics is the process of collecting information about the way a piece of software is used and produced. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.

The analysis of unstructured data types is another challenge getting attention in the industry. Un- structured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc. These challenges are the current inspiration for much of the innovation in modern analytics infor- mation systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation.

One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set. Analytics is increasingly used in education, particularly at the district and government office lev- els. However, the complexity of student performance measures presents challenges when edu- cators try to understand and use analytics to discern patterns in student performance, predict graduation likelihood, improve chances of student success, etc.

One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. In such cases, cloud computing and open source R programming language can help smaller banks to adopt risk analytics and support branch level monitoring by applying predictive analytics. Risks The main risk for the people is discrimination like price discrimination or statistical discrimina- tion.

Analytical processes can also result in discriminatory outcomes that may violate anti-dis- crimination and civil rights laws. There is also the risk that a developer could profit from the ideas or work done by users, like this example: Users could write new ideas in a note taking app, which could then be sent as a custom event, and the developers could profit from those ideas.

This can happen because the ownership of content is usually unclear in the law. Software Analytics Software Analytics refers to analytics specific to software systems and related software develop- ment processes. It aims at describing, predicting, and improving development, maintenance, and management of complex software systems. Software analytics represents a base component of software diagnosis that generally aims at gen- erating findings, conclusions, and evaluations about software systems and their implementation, composition, behavior, and evolution.

Software analytics frequently uses and combines approach- es and techniques from statistics, prediction analysis, data mining, and scientific visualization. For example, software analytics can map data by means of software maps that allow for interactive exploration.

Data plays a critical role in modern software development, be- cause hidden in the data is the information and insight about the quality of software and services, the experience that software users receive, as well as the dynamics of software development. Insightful information obtained by Software Analytics is information that conveys meaningful and useful understanding or knowledge towards performing the target task.

Typically insightful infor- mation cannot be easily obtained by direct investigation on the raw data without the aid of analytic technologies. Actionable information obtained by Software Analytics is information upon which software prac- titioners can come up with concrete solutions better than existing solutions if any towards com- pleting the target task. Software Analytics focuses on trinity of software systems, software users, and software develop- ment process: Software Systems.

Depending on scale and complexity, the spectrum of software systems can span from operating systems for devices to large networked systems that consist of thousands of serv- ers. System quality such as reliability, performance and security, etc.

As the system scale and complexity greatly increase, larger amount of data, e. Users are almost always right because ultimately they will use the software and services in various ways. Therefore, it is important to continuously provide the best experience to users. Usage data collected from the real world reveals how users interact with software and services. The data is incredibly valuable for software practitioners to better understand their cus- tomers and gain insights on how to improve user experience accordingly.

Software Development Process. Software development has evolved from its traditional form to exhibiting different characteristics. The process is more agile and engineers are more collaborative than that in the past. Analytics on software development data provides a powerful mechanism that software practitioners can leverage to achieve higher development productivity. The term has become well known in the software engineering research community after a series of tutorials and talks on software analytics were given by Dr.

Definition According to Gartner analysts Kurt Schlegel, traditional business intelligence were suffering in a lack of integration between the data and the business users. This technology intention is to be more pervasive by real-time autonomy and self-service of data visualization or customization, meanwhile decision makers, business users or even customers are doing their own daily workflow and tasks.

A related field is educational data mining. The definition and aims of Learning Analytics are contested. George Siemens, A more holistic view than a mere definition is provided by the framework of learning analytics by Greller and Drachsler A systematic overview on learning analytics and its key concepts is provided by Chatti et al.

George Siemens takes the position that educational data min- ing encompasses both learning analytics and academic analytics, the former of which is aimed at governments, funding agencies, and administrators instead of learners and faculty.

They go on to attempt to disambiguate educa- tional data mining from academic analytics based on whether the process is hypothesis driven or not, though Brooks questions whether this distinction exists in the literature.

Brooks instead pro- poses that a better distinction between the EDM and LA communities is in the roots of where each community originated, with authorship at the EDM community being dominated by researchers coming from intelligent tutoring paradigms, and learning anaytics researchers being more focused on enterprise learning systems e.

Regardless of the differences between the LA and EDM communities, the two areas have signifi- cant overlap both in the objectives of investigators as well as in the methods and techniques that are used in the investigation. Increasing focus on evidencing progress and professional standards for accountability sys- tems 5.

This focus led to a teacher stakehold in the analytics - given that they are associated with accountability systems 6. Thus an increasing emphasis was placed on the pedagogic affordances of learning analytics 7. This pressure is increased by the economic desire to improve engagement in online educa- tion for the deliverance of high quality - affordable - education History of The Techniques and Methods of Learning Analytics In a discussion of the history of analytics, Cooper highlights a number of communities from which learning analytics draws techniques, including: 1.

Statistics - which are a well established means to address hypothesis testing 2. Business Intelligence - which has similarities with learning analytics, although it has his- torically been targeted at making the production of reports more efficient through enabling data access and summarising performance indicators. Web analytics - tools such as Google analytics report on web page visits and references to websites, brands and other keyterms across the internet.

Operational research - aims at highlighting design optimisation for maximising objectives through the use of mathematical models and statistical methods. Such techniques are im- plicated in learning analytics which seek to create models of real world behaviour for prac- tical application.

Artificial intelligence and Data mining - Machine learning techniques built on data min- ing and AI methods are capable of detecting patterns in data. It is particularly used to ex- plore clusters of networks, influence networks, engagement and disengagement, and has been deployed for these purposes in learning analytic contexts. Information visualization - visualisation is an important step in many analytics for sense- making around the data provided - it is thus used across most techniques including those above.

History of Learning Analytics in Higher Education The first graduate program focused specifically on learning analytics was created by Dr. The fields of learning analytics LA and educational data mining EDM have emerged with the aim of transforming this data into new insights that can benefit students, teachers, and administrators.

Social network analysis tools are commonly used to map social connections and discussions. D-etermination: Decide on the purpose of learning analytics for your institution. E-xplain: Define the scope of data collection and usage. L-egitimate: Explain how you operate within the legal frameworks, refer to the essential legislation. I-nvolve: Talk to stakeholders and give assurances about the data distribution and use. C-onsent: Seek consent through clear consent questions.

A-nonymise: De-identify individuals as much as possible 7. T-echnical aspects: Monitor who has access to data, especially in areas with high staff turn- over. E-xternal partners: Make sure externals provide highest data security standards It shows ways to design and provide privacy conform Learning Analytics that can benefit all stake- holders.

The authors refer to OLA as an ongoing analytics process that encompasses diversity at all four dimensions of the learning analytics refer- ence model. Predictive Analytics Predictive analytics encompasses a variety of statistical techniques from predictive modeling, ma- chine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identi- fy risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for can- didate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score probability for each individual customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecom- munications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the most well known applications is credit scoring, which is used throughout financial ser- vices.

Definition Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns.

Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome.

It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i. Furthermore, the converted data can be used for closed-loop product life cycle im- provement which is the vision of Industrial Internet Consortium.

However, people are increasingly using the term to refer to related analytical disciplines, such as descriptive modeling and decision modeling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmen- tation and decision making, but have different purposes and the statistical techniques underlying them vary.

Predictive Models Predictive models are models of the relation between the specific performance of a unit in a sample and one or more known attributes or features of the unit. The objective of the model is to assess the likelihood that a similar unit in a different sample will exhibit the specific performance.

This category encompasses models in many areas, such as marketing, where they seek out subtle data patterns to answer questions about customer performance, or fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision.

With advancements in computing speed, individual agent modeling systems have become capable of simulating human behaviour or reactions to given stimuli or scenarios. The out of sample bear no chronological relation to the training sample units.

For example, the training sample may consists of literary attributes of writings by Victorian authors, with known attribution, and the out-of sample unit may be newly found writing with unknown authorship; a predictive model may aid in attributing a work to a known author. Another example is given by analysis of blood splatter in simulated crime scenes in which the out of sample unit is the actual blood splatter pattern from a crime scene.

The out of sample unit may be from the same time as the training units, from a previous time, or from a future time. Descriptive Models Descriptive models quantify relationships in data in a way that is often used to classify custom- ers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior such as credit risk , descriptive models identify many different relationships between customers or products.

Descriptive models do not rank-order customers by their likelihood of tak- ing a particular action the way predictive models do. Instead, descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Decision Models Decision models describe the relationship between all the elements of a decision—the known data including results of predictive models , the decision, and the forecast results of the decision—in order to predict the results of decisions involving many variables.

These models can be used in optimization, maximizing certain outcomes while minimizing others. Decision models are gener- ally used to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance. Applications Although predictive analytics can be put to use in many applications, we outline a few examples where predictive analytics has shown positive impact in recent years. Methods of predictive analysis are applied to customer data to pursue CRM objec- tives, which involve constructing a holistic view of the customer no matter where their information resides in the company or the department involved.

CRM uses predictive analysis in applications for marketing campaigns, sales, and customer services to name a few. These tools are required in order for a company to posture and focus their efforts effectively across the breadth of their cus- tomer base. Analytical customer relationship management can be applied throughout the customers lifecycle acquisition, relationship growth, retention, and win-back. Several of the application areas described below direct marketing, cross-sell, customer retention are part of customer relationship management.

Child Protection Over the last 5 years, some child welfare agencies have started using predictive analytics to flag high risk cases. Clinical Decision Support Systems Experts use predictive analysis in health care primarily to determine which patients are at risk of developing certain conditions, like diabetes, asthma, heart disease, and other lifetime illnesses.

Additionally, sophisticated clinical decision support systems incorporate predictive analytics to support medical decision making at the point of care. Osheroff and colleagues: Clinical decision support CDS provides clinicians, staff, pa- tients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. It encompasses a variety of tools and interventions such as computerized alerts and reminders, clinical guidelines, order sets, patient data reports and dashboards, documentation templates, diagnostic support, and clinical workflow tools.

Using large and multi-source imaging, genetics, clinical and demographic data, these investigators developed a decision support system that can predict the state of the disease with high accuracy, consistency and precision. They employed classical model-based and machine learning model-free methods to discriminate between different patient and control groups. Collection Analytics Many portfolios have a set of delinquent customers who do not make their payments on time.

The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identi- fying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs.

Cross-sell Often corporate organizations collect and maintain abundant data e. This directly leads to higher profitability per customer and stronger custom- er relationships. Customer Retention With the number of competing services available, businesses need to focus efforts on maintaining continuous customer satisfaction, rewarding consumer loyalty and minimizing customer attrition. In addition, small increases in customer retention have been shown to increase profits dispropor- tionately.

Businesses tend to respond to customer attrition on a reactive basis, acting only after the customer has initiated the process to terminate service. Proper application of predictive analytics can lead to a more proactive retention strategy. Silent attrition, the behavior of a customer to slowly but steadily reduce usage, is another problem that many companies face. Predictive analyt- ics can also predict this behavior, so that the company can take proper actions to increase custom- er activity.

Direct Marketing When marketing consumer products and services, there is the challenge of keeping up with com- peting products and consumer behavior. Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of product versions, marketing material, communication channels and timing that should be used to target a given consumer. The goal of predictive analytics is typically to lower the cost per order or cost per action. Fraud Detection Fraud is a big problem for many businesses and can be of various types: inaccurate credit applica- tions, fraudulent transactions both offline and online , identity thefts and false insurance claims.

These problems plague firms of all sizes in many industries. Some examples of likely victims are credit card issuers, insurance companies, retail merchants, manufacturers, business-to-business suppliers and even services providers. Predictive modeling can also be used to identify high-risk fraud candidates in business or the public sector.

Mark Nigrini developed a risk-scoring method to identify audit targets.

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