Friday, 31 May 2013

Know the Business Intelligence Technology

Business Intelligence (BI) is one of those terms that conjure up an air of convert operation. However, the core role of the technology is to facilitate decision-making. Like a good spy, once deployed, it assists its "handler" in making informed decisions. Until recently, applications of business intelligence were limited to large and mid-size enterprises, but the technology is gaining popularity. BI promises the type of information support every business manager or decision-maker could use. This article provides an overview of this potentially powerful business tool.
What is business intelligence?
The simple answer is there is no single universally used definition for BI. Developers may define the technology in ways that favor the features of their own brand of business intelligence software. Regardless of the exact wording of a definition, the following applies across BI brands:
  • BI is not a single tool. Most frequently, it is a collection of related technologies, tools and functions. These components explain why the technology is often characterized as a system.
  • The core function in a BI system is to gather, coordinate, and transform data into information efficiently. Not surprisingly, the more integrated the software, the more diverse its data analysis capability.
  • BI deployment is customizable to fit enterprise and industry needs.
In essence, business intelligence is what your enterprise makes of it - depending on usage and functionalities.
What are the key components of a business intelligence system?
What constitutes a BI system varies by developer. However, most integrated systems have the following tools and functionalities:
  • Data management tools for collecting, cleaning, integrating, and analyzing transactional and operational data from multiple sources;
  • A query tool for asking questions from the database;
  • A reporting tool for generating ad hoc reports based on a query or series of queries;
  • An analytical tool for generating projections, conducting forecasts, and creating alternative scenarios;
  • A search tool for locating information, discovering underlying patterns, and augmenting analysis;
  • A dashboard functionality providing for quick viewing and sharing of results.

What are desirable qualities in a business intelligence system?Given the relatively high cost of deploying, running, and maintaining a BI system, it is important that the product you select possesses the following qualities:
  • Ease of use, especially from the point of view of the intended primary users in the enterprise.
  • Flexibility of customization, to improve the best chance of adoption success
  • Best fit to enterprise needs and existing systems, to enhance the ROI of the technology

ConclusionToday's competitive environment requires enterprises to be agile in all aspects of business operations. Few will debate the benefits of having business intelligence technology as part of the performance management toolkit of an enterprise. Properly deployed, BI tools and functionalities improve the timeliness, objectivity and transparency of business decisions. However, realizing these benefits hinges on how well the chosen product fits the specific needs of the enterprise.

Hunt for Intelligent Data Center Automation?

If you are a database administrator, you've probably come across a variety of tools claiming to bring efficiency and manageability to the IT lifecycle. Yet the so called mess of your data center is overwhelming in itself, not to mention concerns regarding shrinking IT budgets, increasing demands and security/integrity of network data. Effectively managing your data center can be overwhelming, especially if there is no IT automation platform currently in place. Many routine tasks are consumed by maintenance operations that could easily be automated. The challenge many IT professionals face is that many networks work off multiple platforms making maintenance and productivity efficiency time consuming and resource draining.
Wouldn't it be wonderful if you could have a central platform for developing and setting policies for data center automation Imagine segregating and prioritizing urgent, manual and time-consuming tasks making data center automation goals not only obtainable, but sophisticated in delivery yet simplistic in use. There are many solutions that provide this level of IT automation, but few provide the predictive analytics and enhance decision automation tools like Stratavia's newest version of Data Palette. If you've ever looked into database automation to address servers, networks and applications, you probably didn't find a solution that could handle the demands of multiple applications, servers and network platforms. The latest release meets these objectives plus offers an intelligent rules engine to align the IT infrastructure with the needs of your business.
Data center automation products should seamlessly combine complex, manually intensive yet repetitive IT administration tasks for a time saving, resource optimizing solutions. IT professionals can now define, build and centrally orchestrate standard operating procedures and report on mission-critical IT operations. Being able to prioritize decision, task and process automation, empowers IT administrators to:
- Dynamically expand, virtualize and allocate server and storage space when needed;
- Automatically trigger alerts and set resolution policies with no manual intervention;
- Administer and track upgrades and configurations;
- Forensically report on database and IT operations for auditing and analysis;
- Deploy patches to prevent security threats and ensure optimal performance; and
- Support adherence to the Information Technology Infrastructure Library (ITIL) framework of best practices.
Combining task, process and decision automation allows junior IT staff to diagnose and address issues without having to escalate them to senior personnel. Wouldn't it be a relief to be able to provide insight and automation that allows you to anticipate data center issues so the infrastructure can proactively support the business? If you're looking to combine multiple levels of automation with the innovative capabilities of predictive analytics, you should really spend some time investigating the Data Palette 4.0.
If you're business is stepping up and you're looking to provide a higher level of automation intelligence to your organizations operational efficiency, data center automation is the key. IT organizations desiring a business driven model that prides itself on smooth, efficient and effective database administration and IT automation, can confidently move away from chaotic, reactive activities.

Enterprise Content Management: Convergence of Structured and Unstructured Data Management

Enterprises are handling increasing amounts of unstructured data (electronic data that are not stored in a predefined structure, like office documents, e-mail, web info), frequently kept in repositories which have structures of limited efficiency & accessibility. Moreover the internal structure of files is usually not standardised and may not be efficient, in terms of information retrieval and reusability. According to international studies, more than 85% of business data are of unstructured nature.
The advent of web content and the necessity to use proactively the web channel in the market, has further increased the need to efficiently manage information content of unstructured nature. The volume of information is rapidly increasing, thus becoming unmanageable (info glut). The increasing need to handle business information efficiently, in a highly competitive environment, has driven business efforts to improve ways of storing, retrieving, analyzing and reusing unstructured data. All relevant efforts aim to develop a meaningful structure which shall accommodate unstructured data. In other words to convert unstructured data to semi-structured data: data having a higher degree of structure than the former (not using a highly granular structure as data stored in fields of a relational database table, however not being stored in a loosely & ineffectively structured data repository).
Traditionally, techniques & technologies used to handle structured data (DBMS, SQL) were incompatible to those used to handle unstructured data (file servers, content management systems, collaboration tools). The term Business Intelligence stems from the structured world while the term Knowledge or Content management stems from the unstructured world. The combined retrieval & analysis of information (e.g. for a Customer) from both structured & unstructured data, has been traditionally carried out manually. However the term business intelligence does no longer refer exclusively to the structured data world. Convergence of structured & unstructured data technologies, is currently experienced. The introduction of a central data repository, can mitigate the negative effect caused by the development of information silos. This applies to both structured and unstructured data assets.
In order to develop a structure for handling unstructured data, an information model needs to be developed. This model has to accommodate the needs of different user groups: customers, info users, content authors, while being structured meaningfully: e.g. per product line, per business process. The use of DTDs (Document Type Definition) or XML schemas to structure content internally by introducing semantic tags, can enhance the capability to retrieve and reuse information hidden in documents. The use of sitemaps, meta tags and RSS feeds has being expanding on the Web, to describe the content of sites, especially on content which is frequently being updated (e.g. news content). RSS allows site syndication, an approach to share content on the web, thus increasing its accessibility & diffusion.

Data Warehousing & Business Intelligence in a Business Perspective

Business Intelligence
Business Intelligence has become a very important activity in the business arena irrespective of the domain due to the fact that managers need to analyze comprehensively in order to face the challenges.
Data sourcing, data analysing, extracting the correct information for a given criteria, assessing the risks and finally supporting the decision making process are the main components of BI.
In a business perspective, core stakeholders need to be well aware of all the above stages and be crystal clear on expectations. The person, who is being assigned with the role of Business Analyst (BA) for the BI initiative either from the BI solution providers' side or the company itself, needs to take the full responsibility on assuring that all the above steps are correctly being carried out, in a way that it would ultimately give the business the expected leverage. The management, who will be the users of the BI solution, and the business stakeholders, need to communicate with the BA correctly and elaborately on their expectations and help him throughout the process.
Data sourcing is an initial yet crucial step that would have a direct impact on the system where extracting information from multiple sources of data has to be carried out. The data may be on text documents such as memos, reports, email messages, and it may be on the formats such as photographs, images, sounds, and they can be on more computer oriented sources like databases, formatted tables, web pages and URL lists. The key to data sourcing is to obtain the information in electronic form. Therefore, typically scanners, digital cameras, database queries, web searches, computer file access etc, would play significant roles. In a business perspective, emphasis should be placed on the identification of the correct relevant data sources, the granularity of the data to be extracted, possibility of data being extracted from identified sources and the confirmation that only correct and accurate data is extracted and passed on to the data analysis stage of the BI process.
Business oriented stake holders guided by the BA need to put in lot of thought during the analyzing stage as well, which is the second phase. Synthesizing useful knowledge from collections of data should be done in an analytical way using the in-depth business knowledge whilst estimating current trends, integrating and summarizing disparate information, validating models of understanding, and predicting missing information or future trends. This process of data analysis is also called data mining or knowledge discovery. Probability theory, statistical analysis methods, operational research and artificial intelligence are the tools to be used within this stage. It is not expected that business oriented stake holders (including the BA) are experts of all the above theoretical concepts and application methodologies, but they need to be able to guide the relevant resources in order to achieve the ultimate expectations of BI, which they know best.
Identifying relevant criteria, conditions and parameters of report generation is solely based on business requirements, which need to be well communicated by the users and correctly captured by the BA. Ultimately, correct decision support will be facilitated through the BI initiative and it aims to provide warnings on important events, such as takeovers, market changes, and poor staff performance, so that preventative steps could be taken. It seeks to help analyze and make better business decisions, to improve sales or customer satisfaction or staff morale. It presents the information that manager's need, as and when they need it.
In a business sense, BI should go several steps forward bypassing the mere conventional reporting, which should explain "what has happened?" through baseline metrics. The value addition will be higher if it can produce descriptive metrics, which will explain "why has it happened?" and the value added to the business will be much higher if predictive metrics could be provided to explain "what will happen?" Therefore, when providing a BI solution, it is important to think in these additional value adding lines.
Data warehousing
In the context of BI, data warehousing (DW) is also a critical resource to be implemented to maximize the effectiveness of the BI process. BI and DW are two terminologies that go in line. It has come to a level where a true BI system is ineffective without a powerful DW, in order to understand the reality behind this statement, it's important to have an insight in to what DW really is.
A data warehouse is one large data store for the business in concern which has integrated, time variant, non volatile collection of data in support of management's decision making process. It will mainly have transactional data which would facilitate effective querying, analyzing and report generation, which in turn would give the management the required level of information for the decision making.
The reasons to have BI together with DW
At this point, it should be made clear why a BI tool is more effective with a powerful DW. To query, analyze and generate worthy reports, the systems should have information available. Importantly, transactional information such as sales data, human resources data etc. are available normally in different applications of the enterprise, which would obviously be physically held in different databases. Therefore, data is not at one particular place, hence making it very difficult to generate intelligent information. The level of reports expected today, are not merely independent for each department, but managers today want to analyze data and relationships across the enterprise so that their BI process is effective. Therefore, having data coming from all the sources to one location in the form of a data warehouse is crucial for the success of the BI initiative. In a business viewpoint, this message should be passed and sold to the managements of enterprises so that they understand the value of the investment. Once invested, its gains could be achieved over several years, in turn marking a high ROI.
Investment costs for a DW in the short term may look quite high, but it's important to re-iterate that the gains are much higher and it will span over many years to come. It also reduces future development cost since with the DW any requested report or view could be easily facilitated. However, it is important to find the right business sponsor for the project. He or she needs to communicate regularly with executives to ensure that they understand the value of what's being built. Business sponsors need to be decisive, take an enterprise-wide perspective and have the authority to enforce their decisions.
Process
Implementation of a DW itself overlaps with some phases of the above explained BI process and it's important to note that in a process standpoint, DW falls in to the first few phases of the entire BI initiative. Gaining highly valuable information out of DW is the latter part of the BI process. This can be done in many ways. DW can be used as the data repository of application servers that run decision support systems, management Information Systems, Expert systems etc., through them, intelligent information could be achieved. But one of the latest strategies is to build cubes out of the DW and allow users to analyze data in multiple dimensions, and also provide with powerful analytical supporting such as drill down information in to granular levels. Cube is a concept that is different to the traditional relational 2-dimensional tabular view, and it has multiple dimensions, allowing a manager to analyze data based on multiple factors, and not just two factors. On the other hand, it allows the user to select whatever the dimension he wish to choose for analyzing purposes and not be limited by one fixed view of data, which is called as slice & dice in DW terminology.
BI for a serious enterprise is not just a phase of a computerization process, but it is one of the major strategies behind the entire organizational drivers. Therefore management should sit down and build up a BI strategy for the company and identify the information they require in each business direction within the enterprise. Given this, BA needs to analyze the organizational data sources in order to build up the most effective DW which would help the strategized BI process.
High level Ideas on Implementation
At the heart of the data warehousing process is the extract, transform, and load (ETL) process. Implementation of this merely is a technical concern but it's a business concern to make sure it is designed in such a way that it ultimately helps to satisfy the business requirements. This process is responsible for connecting to and extracting data from one or more transactional systems (source systems), transforming it according to the business rules defined through the business objectives, and loading it into the all important data model. It is at this point where data quality should be gained. Of the many responsibilities of the data warehouse, the ETL process represents a significant portion of all the moving parts of the warehousing process.
Creation of a powerful DW depends on the correctness of data modeling, which is the responsibility of the database architect of the project, but BA needs to play a pivotal role providing him with correct data sources, data requirements and most importantly business dimensions. Business Dimensional modeling is a special method used for DW projects and this normally should be carried out by the BA and from there onwards technical experts should take up the work. Dimensions are perspectives specific to a business that could be used for analysis purposes. As an example, for a sales database, the dimensions could include Product, Time, Store, etc. Obviously these dimensions differ from one business to another and hence for each DW initiative those dimensions should be correctly identified and that could be very well done by a person who has experience in the DW domain and understands the business as well, making it apparent that DW BA is the person responsible.
Each of the identified dimensions would be turned in to a dimension table at the implementation phase, and the objective of the above explained ETL process is to fill up these dimension tables, which in turn will be taken to the level of the DW after performing some more database activities based on a strong underlying data model. Implementation details are not important for a business stakeholder but being aware of high level process to this level is important so that they are also on the same pitch as that of the developers and can confirm that developers are actually doing what they are supposed to do and would ultimately deliver what they are supposed to deliver.
Security is also vital in this regard, since this entire effort deals with highly sensitive information and identification of access right to specific people to specific information should be correctly identified and captured at the requirements analysis stage.
Advantages
There are so many advantages of BI system. More presentation of analytics directly to the customer or supply chain partner will be possible. Customer scores, customer campaigns and new product bundles can all be produced from analytic structures resulting in high customer retention and creation of unique products. More collaboration within information can be achieved from effective BI. Rather than middle managers getting great reports and making their own areas look good, information will be conveyed into other functions and rapidly shared to create collaborative decisions increasing the efficiency and accuracy. The return on human capital will be greatly increased.
Managers at all levels will save their time on data analysis, and hence saving money for the enterprise, as the time of managers is equal to money in a financial perspective. Since powerful BI would enable monitoring internal processes of the enterprises more closely and allow making them more efficient, the overall success of the organization would automatically grow. All these would help to derive a high ROI on BI together with a strong DW. It is a common experience to notice very high ROI figures on such implementations, and it is also important to note that there are many non-measurable gains whilst we consider most of the measurable gains for the ROI calculation. However, at a stage where it is intended to take the management buy-in for the BI initiative, it's important to convert all the non measurable gains in to monitory values as much as possible, for example, saving of managers time can be converted in to a monitory value using his compensation.
The author has knowledge in both Business and IT. Started career as a Software Engineer and moved to work in the business analysis area of a premier US based software company.

Data and Confidentiality Management

Data is one of the crucial and confidential aspects of any business. The nature and sensitivity related to the information makes a highly challenging job.
"Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise."
Above mentioned definition is broad and encompasses a number of professions (JOBS) which may not having direct technical linkups with lower-level aspects, such as inter-intra relational database management. According to the definition provided by the DAMA Data Management Body of Knowledge (DAMA-DMBOK) is: "Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets."
Data Management Jobs comprise in many departments: (A Long Networking of Jobs)
1. Data governance
2. Data Architecture, Analysis and Design
3. Database Management
4. Data Security Management
5. Data Quality Management
6. Document, Record and Content Management
7. Data Warehousing and Business Intelligence Management
8. Meta Data Management
9. Reference and Master Data Management
10. Contact Data Management
These are some the listed units, where grouped by the DAMA DMBOK Framework.
Under management usage, one needs to easily distinguish a regular trend away from the given 'data' in compound terminology to the expression information or within the knowledge when assisting in non-technical context. It exists not only jobs, but also information and knowledge management. Somehow the traditional data that is managed or processed over the second looks. Apart from the processing, here the job is keeping the extreme relevant distinction between data and derived values in the information scale. While Data is secret, so information and management employees pursue the job in private channels. It needs to be maintained for long term and sustained till the employer's exit.
Taking the note from DAMA DMBOK, are entitled with the development, implementation and management of plans, policies, programs and practices that organize, defend, distribute and enhance the worth of data and information sources.
The role of data manager is to track and entrust to the desired database, the data that gathered from initial operation and research. Research roots from the organization to organization, in clinical or medical, Services may be bit different. As they need to be ensured about the basic comprehensiveness, accuracy and reliability of the data, so that it can meet the values of expected organizations.
Data manager is responsible for process sing data, using a range of computer applications and database systems to hold up compilation, clear out and supervision of subject or parent data.

What is the Best Way to Display Business Intelligence Data?

Visualization is a key component of all business intelligence solutions, whether you're dealing with Microsoft SQL Server, Oracle, or SAP Crystal Reports. Business intelligence data can be displayed in a manner as simple as rows and cells on a Microsoft Excel spreadsheet, to something complex like an interactive map or three-dimensional bars and graphs with a dynamic visual tool like Microsoft Silverlight. Obviously, the key factor is that the data is understood as quickly and comprehensively as possible, so what's the best route to get there?
It may sound like a cop-out to those who are currently struggling with this issue, but the answer is: it depends.
That's not a cop-out, and it's not skirting the issue; it's the truth. Different audiences are going to interpret data visualizations in different ways. To a doctor or someone who works at an architecture firm, analyzing three-dimensional data is likely the norm. They've gone through years of school to hone their senses and explore the worlds of 3D imaging. It's nothing new for them, and they're likely succeeding in those fields because their brains work a certain way in the first place.
For others - account executives, project managers, accountants, etc. - something two-dimensional or more basic might be the way to go. That's not to say that these people are less intuitive or less intelligent than the 3D crowd by any means, but their brains may simply be used to processing data in a different manner. Both groups possess analytical intuition around various areas of their jobs that would likely be lost on each other. Different industries, different minds... different course of action for data visualization.
If you're working on developing business intelligence solutions for a client, stop and ask yourself this: What kind of people are going to be using this data? What do I know about my intended audience? If the answer is that you don't know very much, then perhaps you need to take a step back and consult with your client before determining the best course of action. Proceeding onward with a flashy, visually striking 3D plan might seem like the best way to "wow" your client and knock their proverbial socks off, but the key factor is to make sure the data is actually presented in a way that makes sense to them.
In the long run, showing off for the sake of showing off by developing something that's flashy and eye-catching might not impress at all. As a developer or a designer, you may find yourself thinking that the dynamic solution you're working on would be impressive if it were handed to you, but remember - it's about what looks good and what's clear to them.
Take some time. Get to know your audience. Get to know what they're looking for before you dive headfirst into what could be treacherous waters. The most intelligent business intelligence solutions aren't necessary the flashiest; they're the ones that earn you a strong recommendation and a place among your clients' most esteemed partners.