Intelligence Archives - GUJRAT INDIA

Artificial Intelligence and Machine Learnings Are Changing Our Life

Last month, I was discussing with one of my customers. During the discussion, I found that Cloud and SaaS applications have changed the business landscape and helping Business Owners. Post Cloud ERP solution, my Customer was saving around USD 2000 per month by not having Hardware and it’s maintenance, IT Persons etc.

I was just wondering how Artificial Intelligence (AI) and Machine Learning (ML) could change the life of Business Owners and industry? Today we see them in every stages of our life, but can’t distinguish them separately. They are part of our life.

How AI and ML are disrupting the industry?

AI, in manufacturing industry along with IOT tools, is suggesting CNC Machines on what to produce, when to produce and how much to produce. This has improved the efficiency and usage of machines, manpower etc. The production line has become smooth without many surprises as the Demand and Supply are met by ERP and IOT as this decision information are passed to different touch points that would execute the same. Define a standard patterns and rest is done automatically. Hardware and its preventive maintenance are controlled and maintenance to AI, its Data Centre issues and maintenance are impacted.

AI and ML are changing the way we are buying items, doing Sales, Buying rental or selling houses etc. Even dating applications are helping many to match and find the right partners. AI and ML’s are controlling Amazon’s recommendation of book, Google’s suggesting right information or pushing Ads etc. Jobs market is shrinking. Lot of mundane work is replaced with technical tools.

AI and ML have improved the life of online shoppers with right messages, supporting information on the additional items etc. Now ML is automatically learns the user patterns and suggest what to purchase. These tools have helped many online eCommerce companies to target the products to right cohort of Customers and learn more about their purchase habits, when and where they can purchase etc.

Based on your “social” credit history few companies helps to get you Loans or ensure that you get housing etc (Ant Financials)

AI tools have disrupted Agriculture sector too. The activities that are too data sensitive, difficult to sit and analyse have been simplified now. Now we can measure and monitor crops yields, productivity, soil fertility and lack of nutrition etc. Many Agritech companies are depending on the AI and ML to feed the data back to algorithm and see what happens.

How our personal life is disrupted to make it simple?

Earlier, we use to hail for the taxies by roadside or call Taxi Companies to book a Taxi. Now we use Apps and no more hailing by the roadside. UBER, Ola, GoJek have changed the need to own a car and pay for the maintenance. Money saving is huge considering savings in car maintenance cost, insurance and parking charges etc. This reduced the traffic on the road and getting blocked during peak hours, saving millions of Dollars in fuels.

Google is releasing the Driverless Car economy. AI’s supervised learning helps to check the car movement; it’s surroundings, nearby cars or any other vehicles etc. This is helping to protect the passengers and other surrounding properties. Already UBER is talking about buying these cars for their fleets and also developing its own Driverless Cars.

AirBnB and Oyo have increased the quality of the hotel’s experiences, while prices are pushed down. OYO redefined the End users experience at Hotel by uplifting the standard of rooms and services. Guest is ensured to receive the same treatment across multiple hotels. Now a day customers are NOT checking Hotel name and its credibility etc. Customer is checking whether the hotel is qualified by OYO or not. That assures the quality and minimum standard of services.

Many large companies that don’t own any thing, but they are controlling the majority of our life

1. Uber, Ola – Car services company, that integrated the distributed taxies

2. AirBnB and Oyo – Helped to integrate the smaller hotels and increased customer experiences.

3. Google – Search engine and Ads

4. Amazon Web Services, Microsoft Azure, Google Cloud – Provides the infrastructure to manage and run the applications at the fraction of original cost of buying a server.

So what is happening here?

Many IT Jobs are already suffering. Development tools are reducing the number of Developers required, Selenium like tools are replacing the Manual testing. We have built the tools that reduces the web application testing duration by 80%.

But the side effect of this is, many have lost jobs and income generation opportunities.

How Machine Learning works?

If you see the above business they don’t own any properties, but they control the activities as they use AI and ML that gives the information back to the application. One of the ways ML works is, it need a huge amount of transaction data to analyse and arrive at the patterns and learn from it.

To make the Machine learning’s Algorithm to work it requires a huge amount of data and feedback for it to create its own patterns, test, learn and deploy new ones. Many healthcare start-ups are using the AI and ML to scan the digitized the reports and it feeds the data to AI and its recreating the models to analyse.

Beginners’ Guide to Business Intelligence Solutions

Current Condition of the Business Intelligence Tools Market

The sustained interest in Business Intelligence applications has driven large corporations, offshore software development centers as well as custom software development companies to focus on developing a wide range of Business Intelligence Tools suitable for each and every industry. The use of Business Intelligence tools in key industries from aerospace to iron and steel has also increased in recent years due to the uncertainty in global markets. Currently available tools including the Microsoft Business Intelligence software include numerous paid, freeware as well as open source and proprietary software, which are often customized by a custom software developer to suit the requirements of a specific client. Some of the additional categories of Business Intelligence Tools are discussed here and these constitute only a few of the business intelligence reporting tools commonly utilized by the enterprise.

Data Mining

Data mining combines key elements of statistics and computer science with the objective of identifying patterns in large data sets. Currently implemented data mining methodology includes various elements of database systems, statistics, machine learning and artificial intelligence to deliver actionable intelligence to managers, decision makers and data analysts in an enterprise. Apart from the analysis of the available raw data, additional operations performed by data mining process include online updating, visualization, discovered structure post-processing, complexity considerations, metrics to determine interest as well as data management. Data mining is distinct from information processing or large-scale data analysis as the process is based on “discovery” i.e. the detection of something new. As data mining deals with large data sets, various automated and semi-automated solutions are available to carry out the task. Data mining applications developed by any software development company focuses on performing the following tasks- anomaly detection, association rule learning, clustering, classification, regression as well as summarization. Current business applications include data mining in applications related to customer relationship management, determination of successful employee characteristics using HR department data, identification of customer purchase pattern by the marketing department as well as much more. Leading companies engaged in providing data mining tools for use in business intelligence reporting include Extra-Data Technologies, Clarabridge, Versium Analytics, emanio and Polygraph Media.

Data Warehousing

Data Warehousing in simple terms refers to any database utilized for reporting as well as analyzing enterprise data. The data in an enterprise is usually obtained from all over the organization including the HR, Marketing, Sales, Customer Support, Warehouse, administration departments. In some cases, the raw data may undergo a small degree of pre-processing prior to being used for reporting in a Data Warehouse. A traditional data warehouse (a warehouse operating on the extract-transform-load mechanism), houses the key functions by using separate staging, integration and access layers. The staging area stores all the raw data obtained from various enterprise-wide sources. In the integration layer the raw data stored in the staging area is integrated to transform it into a form suitable for analysis and stored in the data warehouse database. The data stored in the data warehouse database is arranged in hierarchical groups, which are accessible by the user through the access layer. Each data warehouse is often subdivided into data marts, which store subsets of the data integrated in the warehouse. The key objective of a data warehouse is thus to store data in a format suitable for analysis by the user using various techniques including OLAP and data mining.

The earliest data warehouses used by an organization were offline operational data warehouses. In these warehouses, the data was updated periodically (fortnightly, weekly or monthly) from operational systems and stored in a report-oriented format. In the next stage of data warehouse evolution, offline data warehouses came into existence. In offline data warehouses, the data was updated regularly from operational systems and the structure of the stored data was designed to aid the reporting process. The offline data warehouses later evolved into Online Integrated Data Warehouses, which updates the data in the warehouse in real-time by recording every transaction performed on the source data. Further evolution of data warehouses has resulted in the creation of the integrated data warehouse, which compiles the data obtained from the various departments of the enterprise to provide users with real-time access to actionable intelligence from all over the organization. Leading data warehousing solutions companies include Accenture, IBM, Igate and Infobright.

Decision Engineering

Decision Engineering is defined as a framework, which unifies various leading practices in the field of enterprise decision-making to improve the overall decision-making procedure by providing a structured approach. The decision engineering process is designed to overcome problems resulting from a “complexity ceiling” of the decision-making process. This “complexity ceiling” usually results from a mismatch between the complexity of a particular situation and the sophistication of the decision-making procedure being implemented. Decision engineering acts as a framework for providing advanced analytic techniques to a non-enterprise user while simultaneously integrating machine learning and inductive reasoning techniques to streamline the organizational decision-making procedure. The use of Decision Engineering as a business intelligence tool by enterprises is still in its infancy and further development would be required before decision engineering develops into a viable business intelligence reporting tool.

Reporting and Querying Software

Reporting and querying software are designed to provide users with access to the data stored on enterprise databases subsequent to submission of user-queries. Such tools are designed to provide a logical format to the available data sets to support enterprise-wide data accessibility as well as speed-up the organizational decision-support process. Currently, various open source business intelligence tools as well as commercial business intelligence reporting software are developed by software development companies all over the world. Some of the leading reporting and querying tools are mPower, Zoho Reports, Cognos BI, GNU Enterprise and JasperReports. Many offshore software development companies in India also provide customized versions of reporting and querying software to streamline the overall enterprise-wide decision making process.

Spreadsheets

A spreadsheet is defined as an interactive computer program, which allows the analysis of available information by use of a tabular format, which originated from the use of paper-based accounting spreadsheets. On a spreadsheet, users can modify the values in each cell of the spreadsheet and are now used widely by the financial sector as a replacement of paper-based accounting methods. The digital spreadsheets allow users to automatically calculate values after making modifications to the available data as and when necessary. Apart from the standard arithmetic calculation support, currently available spreadsheets also features support for a wide range of statistical and financial operations built into this commonly used business intelligence tool. Spreadsheets are probably the most widely used and easily available among a wide range of proprietary and open source business intelligence tools. The first spreadsheet introduced for a micro computer was Visicalc, which was overtaken by Lotus 1-2-3 at a later date. Currently Microsoft Excel, available as part of the Microsoft Office Package, is the leading spreadsheet solution utilized by enterprises all over the world.

Business Agility in a World of Artificial Intelligence

Business Agility, AI and Remaining Human

“The automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this destruction deep into the middle classes, with only the most intelligent, caring and supervisory roles remaining.” Stephen Hawking.

It doesn’t take one of the world’s greatest living astrophysicists to understand there is a major shift going on in not only our workplaces, but in our society as a whole. The difference between what humans can do and what machines and computers are capable of is shifting, and at an accelerating rate. This reality becomes truly scary to those who currently earn a living by doing repetitive tasks or thinking in repeatable patterns; in other words, most of us.

If you are not able to distinguish what you do from that of a machine or a computer, then how can you really call yourself much more than a human doing? To remain a human being requires more!

The difference between a human doing and a human being?

Your ability to feel and relate.

Going forward, this will be most obvious in those roles that as Professor Hawking reminds us require feelings, leadership and creativity combined with intelligence. For the foreseeable future, this means that your economy will increasingly be influenced by your ability to listen, understand, empathize, create and lead. In short, the more you cultivate your ability to consciously feel, powerfully communicate and relate, the better chance you will have of getting paid. Transacting can be left to our increasingly sophisticated creations.

Even if increasing your ability to use your senses to relate is reduced down to the economics of being employed or not, just that is a positive start! Most of us are now being forced to learn that trying to compete with computers and machines only leads to increased stress and ultimately dis-ease.

“Life in a Spreadsheet”

A good friend, Tim Finucane, came up with this appropriate metaphor over ten years ago, and it rings truer today than when he first coined it. Since the advent of Lotus 1-2-3 and Microsoft Excel, 30 plus years ago, we have been able to measure job performance with increasing accuracy as well as more intrusiveness. Whereas, spreadsheets were first used to help us perform better, they have now morphed into being used to dictate and monitor increasingly challenging performance metrics. Is it any wonder that each little box in a spreadsheet is called a “cell”? Just like prison, these cells keep getting smaller and just like government budgets, each metric usually increases over time.

Spreadsheet technology gave way to the idea of Key Performance Indicators or KPIs. For your company, these are metrics based almost entirely upon historical performance, yet are prone to increase or tighten every time they are reviewed. This is fine for a machine that you can tweak and improve with newer technology, but when the key components are you and your co-worker, constant increases can stifle your creativity and crush your ability to care. The machine literally drains you of your humanity and what are human beings without that?

Powerful forces, problem or Agile Opportunity?

Thus we have two powerful forces working against us. Firstly, constantly increasing performance metrics keep limiting our ability to be human. Secondly, increasingly efficient computers and machines make obsolete more of our opportunities to earn. The good news is that those who understand these powerful forces and the change they are bringing can begin directly to increase their creativity, as well as hone their ability to sense, relate and lead.

What if this measurement trend also is forcing us to take more personal responsibility to relearn and improve the skills necessary for not just emotional, but social competence? Don’t think this is important? One of the seminars held at this year’s Davos World Economic Forum was titled, “Maintaining Your Humanity“. Even the Elites now get it.

Competing with an increasingly sophisticated computer or machine for jobs that technology can do better is not a winning strategy! Especially if you wish to remain healthy and prosperous. The one area that for the foreseeable future will remain the domain of humans is where feelings and relationships come into play. These areas include but are not limited to:

Customer Service
Healthcare
Sales
Leadership
Music and arts

Each one of these areas of human endeavor requires feeling and sensitivity to succeed. Computers and machines cannot do that. Machines can measure and they can perform without rest, but they cannot not feel anything while performing or when they objectively measure and communicate the results. This job is left for us to interpret and enjoy, or not.

Conclusion: Remain Human, Get Agile, or Be Swallowed by the Technology

If you want to insure your ability to earn a living going forward, you need to begin now to optimize your use of computer and machine skills, while simultaneously rediscovering and mastering your ability to be vibrant human being. Missing this opportunity may not affect you tomorrow, but sooner or later the Technologically Weighted Future we are all tumbling into will catch up to even you!

Quick Guide to Implementing Business Intelligence, Data Warehousing & BPM

Definitions and Overview

Business Performance Management (BPM) establishes a framework to improve business performance by measuring key business characteristics which can be used to feedback into the decision process and guide operations in an attempt to improve strategic organisational performance. Other popular terms for this include; Enterprise PM (EPM), Corporate PM (CPM) Enterprise Information Systems (EIS), Decision Support Systems (DSS), Management Information Systems (MIS).

BPM: Cycle of setting objectives, monitoring performance and feeding back to new objectives.

Business Intelligence (BI) can be defined as the set of tools which allows end-users easy access to relevant information and the facility to analyse this to aid decision making. More widely the ‘intelligence’ is the insight which is derived from this analysis (eg. trends and correlations).

BI: Tools to Access & Analyse Data

Key Performance Indicators (KPIs) are strategically aligned corporate measures that are used to monitor, predict and anticipate the performance of the organisation. They form the basis of any the BPM solution and in an ideal world it should be possible to relate strategic KPIs to actual operational performance within the BI application.

KPIs provide a quick indication on the health of the organisation and guide management to the operational areas affecting performance.

In many companies analysis of data is complicated by the fact that data is fragmented within the business. This causes problems of duplication, inconsistent definitions, inconsistency, inaccuracy and wasted effort.

Silos of Data: Fragmented, Departmental Data Stores, often aligned with specific business areas.

Data Warehousing (DWH) is often the first step towards BI. A Data Warehouse is a centralised pool of data structured to facilitate access and analysis.

DWH: Centralised/Consolidated Data Store

The DWH will be populated from various sources (heterogeneous) using an ETL (Extract, Transform & Load) or data integration tool. This update may be done in regular periodic batches, as a one off load or even synchronised with the source data (real time).

ETL: The process of extracting data from a source system, transforming (or validating) it and loading it into a structured database.

A reporting (or BI) layer can then be used to analyse the consolidated data and create dashboards and user defined reports. A modelling layer can be used to integrate budgets and forecasting.

As these solutions get more complex, the definitions of the systems and what they are doing becomes more important. This is known as metadata and represents the data defining the actual data and its manipulation. Each part of the system has its own metadata defining what it is doing. Good management & use of metadata reduces development time, makes ongoing maintenance simpler and provides users with information about the source of the data, increasing their trust and understanding of it.

Metadata: Data about data, describing how and where it is being used, where it came from and what changes have been made to it.

Commercial Justifications

There is clear commercial justification to improve the quality of information used for decision making. A survey conducted by IDC found that the mean payback of BI implementation was 1.6 years and that 54% of businesses had a 5 year ROI of >101% and 20% had ROI > 1000%.

ROI on BI > 1000% from 20% of organisations

There are now also regulatory requirements to be considered. Sarbanes-Oxley requires that US listed companies disclose and monitor key risks and relevant performance indicators – both financial and non financial in their annual reports. A robust reporting infrastructure is essential for achieving this.

SarbOx requires disclosure of financial & non-financial KPIs

Poor data quality is a common barrier to accurate reporting and informed decision making. A good data quality strategy, encompassing non system issues such as user training and procedures can have a large impact. Consolidating data into a DWH can help ensure consistency and correct poor data, but it also provides an accurate measure of data quality allowing it to be managed more pro-actively.

Data Quality is vital and a formal data quality strategy is essential to continually manage and improve it.

Recent research (PMP Research) asked a broad cross section of organisations their opinion of their data quality before and after a DWH implementation.

– “Don’t know” responses decreased from 17% to 7%

– “Bad” or “Very Bad” decreased from 40% to 9%

– Satisfactory (or better) increased from 43% to 84%

DWH implementations improve Data Quality.

Tools Market Overview

At present BI is seen as a significant IT growth area and as such everyone is trying to get onto the BI bandwagon:

ERP tools have BI solutions e.g SAP BW, Oracle Apps

CRM tools are doing it: Siebel Analytics,

ETL vendors are adding BI capabilities: Informatica

BI vendors are adding ETL tools: Business Objects (BO) Data Integrator (DI), Cognos Decision Stream

Database vendors are extending their BI & ETL tools:

Oracle: Oracle Warehouse Builder, EPM

Microsoft: SQL 2005, Integration Services, Reporting Services, Analytical Services

Improved Tools

Like all maturing markets, consolidation has taken place whereby fewer suppliers now cover more functionality. This is good for customers as more standardisation, better use of metadata and improved functionality is now easily available. BI tools today can now satisfy the most demanding customer’s requirements for information.

Thinking and tools have moved on – we can now build rapid, business focussed solutions in small chunks – allowing business to see data, store knowledge, learn capabilities of new tools and refine their requirements during the project! Gone are the days of the massive data warehousing project, which was obsolete before it was completed.

A typical DWH project should provide usable results within 3 – 6 Months.

Advice & Best Practice

Initial Phase

Successful BI projects will never finish. It should perpetually evolve to meet the changing needs of the business. So first ‘wins’ need to come quickly and tools and techniques need to be flexible, quick to develop and quick to deploy.

Experience is Essential

Often we have been brought in to correct failed projects and it is frightening how many basic mistakes are made through inexperience. A data warehouse is fundamentally different to your operational systems and getting the initial design and infrastructure correct is crucial to satisfying business demands.

Keep Internal Control

We believe that BI is too close to the business and changes too fast to outsource. Expertise is required in the initial stages, to ensure that a solid infrastructure is in place (and use of the best tools and methods.) If sufficient experience is not available internally external resource can be useful in the initial stages but this MUST include skills transfer to internal resources. The DWH can then grow and evolve (with internal resourcing) to meet the changing needs of the business.

Ensure Management and User Buy In

It may sound obvious but internal knowledge and support is essential for the success of a DWH, yet ‘Reporting’ is often given a low priority and can easily be neglected unless it is supported at a senior business level. It is common to find that there is a limited knowledge of user requirements. It is also true that requirements will change over time both in response to changing business needs and to the findings/outcomes of the DWH implementation and use of new tools.

Strong Project Management

The complex and iterative nature of a data warehouse project requires strong project management. The relatively un-quantifiable risk around data quality needs managing along with changing user requirements. Plan for change and allow extra budget for the unexpected. Using rapid application development techniques (RAD) mitigates some of the risks by exposing them early in the project with the use of proto-types.

Educating the End Users

Do not under estimate the importance of training when implementing a new BI/ DWH solution. Trained users are 60% more successful in realising the benefits of BI than untrained users. But this training needs to consider specific data analysis techniques as well as how to use the BI tools. In the words of Gartner, “it is more critical to train users on how to analyse the data.” Gartner goes on to say “… that focusing only on BI tool training can triple the workload of the IT help desk and result in user disillusionment. A user who is trained on the BI tool but does not know how to use it in the context of his or her BI/DWH environment will not be able to get the analytical results he or she needs…”. Hence bespoke user training on your BI system and data is essential.

Careful planning of the training needs and making the best use of the different training mediums now available can overcome this issue. Look for training options such as: Structured classroom (on or off site), web based e-learning (CBT), on the job training & skills transfer, bespoke training around your solution & data.

Technical Overview

Information Portal: This allows users to manage & access reports and other information via a corporate web portal. As users create & demand more reports the ability to easily find, manage & distribute them is becoming more important.

Collaboration: The ability for the Information Portal to support communication between relevant people centred around the information in the portal. This could be discussion threads attached to reports or workflow around strategic goal performance.

Guided Analysis: The system guides users where to look next during data analysis. Taking knowledge from people’s heads and placing it in the BI system.

Security: Access to system functionality and data (both rows and columns) can be controlled down to user level and based on your network logon.

Dashboards & Scorecards:

Providing management with a high level, graphical view of their business performance (KPIs) with easy drill down to the underlying operational detail.

Ad-hoc Reporting and Data Analysis: End users can easily extract data, analyse it (slice, dice & drill) and formally present it in reports & distribute them.

Formatted/ Standard Reports: Pre-defined, pixel perfect, often complex reports created by IT. The power of end user reporting tools and data warehousing is now making this type of report writing less technical and more business focussed.

Tight MS Office integration: More users depend on MS Office software, therefore the BI tool needs to seamlessly link into these tools.

Write Back: The BI portal should provide access to write back to the database to maintain: reference data, targets, forecasts, workflow.

Business Modelling/ Alerting: around centrally maintained data with pre-defined, end user maintained, business rules.

Real Time: As the source data changes it is instantly passed through to the user. Often via message queues.

Near Real Time: Source data changes are batched up and sent through on a short time period, say every few minutes – this requires special ETL techniques.

Batch Processing: Source Data is captured in bulk, say overnight, whilst the BI system is offline.

Relational Database Vs OLAP (cubes, slice & dice, pivot)

This is a complex argument, but put simply most things performed in an OLAP cube can be achieved in the relational world but may be slower both to execute and develop. As a rule of thumb, if you already work in a relational database environment, OLAP should only be necessary where analysis performance is an issue or you require specialist functionality, such as budgeting, forecasting or ‘what if’ modelling. The leading BI tools seamlessly provide access to data in either relational or OLAP form, making this primarily a technology decision rather than a business one.

Top Down or Bottom Up Approach?

The top down approach focuses on strategic goals and the business processes and organisational structure to support them. This may produce the ideal company processes but existing systems are unlikely to support them or provide the data necessary to measure them. This can lead to a strategy that is never adopted because there is no physical delivery and strategic goals cannot be measured.

The bottom up approach takes the existing systems and data and presents it to the business for them to measure & analyse. This may not produce the best strategic information due to the limited data available and data quality.

We recommend a compromise of both approaches: Build the pragmatic bottom up solution as a means to get accurate measures of the business and a better understanding of current processes, whilst performing a top down analysis to understand what the business needs strategically. The gap analysis of what can be achieved today and what is desired strategically will then provide the future direction for the solution and if the solution has been designed with change in mind, this should be relatively straight forward, building upon the system foundations already in place.

Advanced Business Intelligence

The following describes some advanced BI requirements that some organisations may want to consider: Delivering an integrated BPM solution which has business rules and workflow built in allowing the system to quickly guide the decision maker to the relevant information.

Collaboration and Guided Analysis to help manage the action required as a result of the information obtained.

More user friendly Data Mining and Predictive Analytics, where the system finds correlations between un-related data sets in order to find the ‘golden nugget’ of information.

More integration of BI information into the Front Office Systems e.g. a gold rated customer gets VIP treatment when they call in, data profiling to suggest this customer may churn, hence offer them an incentive to stay.

Increased usage of Real Time data.

End to end Data Lineage automatically captured by the tools. Better metadata management of the systems will mean that users can easily see where the data came from and what transformations it has undergone, improving the trust in the data & reports. Systems will also be self documenting providing users with more help information and simplifying ongoing maintenance.

Integrated, real time Data Quality Management as a means to measure accuracy of operational process performance. This would provide cross system validation, and verify business process performance by monitoring data accuracy, leading to better and more dynamic process modelling, business process re-engineering and hence efficiency gains.

Packaged Analytical Applications like finance systems in the 80’s and packaged ERP (Enterprise Requirement Planning) in the 90’s. Packaged BI may become the standard for this decade. Why build your own data warehouse and suite of reports and dashboards from scratch when your business is similar to many others? Buy packaged elements and use rapid deployment templates and tools to configure them to meet your precise needs. This rapid deployment capability then supports you as your business evolves.

BI for the masses: As information becomes more critical to manage operational efficiencies, more people need access to that information. Now the BI tools can technically and cost effectively provide more people with access to information, BI for the masses is now reality and can provide significant improvement to a business. The increased presence of Microsoft in the BI space will also increase usage of BI and make it more attractive. BusinessObjects’ acquisition of Crystal and recent release of XI will also extend BI to more people, in and outside the organisation – now everyone can be given secure access to information!

Conclusion

The potential benefits from a BI/DWH implementation are huge but far too many companies fail to realise these through: lack of experience, poor design, poor selection and use of tools, poor management of data quality, poor or no project management, limited understanding of the importance of metadata, no realisation that if it is successful it will inevitably evolve and grow, limited awareness of the importance of training….. with all these areas to consider using a specialist consultancy such as IT Performs makes considerable sense.