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Worst Practices while deploying a Predictive Model Contd..

27 Jul

In my previous article we saw what are all the worst approaches followed by organizations while deploying a Predictive analytic project. This article will provide you information on how to deploy successful predictive analytics model.

Successful Predictive Analytics Deployment

Now that we’ve discussed the wrong approach to predictive analytics, let’s look at some of the critical steps that must be taken to ensure its success.

Understanding the Business Need

As mentioned earlier, it is crucial for companies to identify the drivers behind the predictive analytics project in the early planning stages. Once an organization defines what new information it is trying to uncover, what new facts it wants to learn, or what business initiatives need to be enhanced, it can build models and deploy results accordingly.

 Understanding the Data

A thorough collection and exploration of the data should be performed. This enables those who are building the application to get familiar with the information at hand, so they can identify quality issues, glean initial insight, or detect relevant subsets that can be used to form hypotheses suggested by the experts for hidden information. This also ensures that the available data will address the business objective.

 Preparing the Data

To get data ready, IT organizations must select tables, records, and attributes from various sources across the business. Data must be transformed, merged, aggregated, derived, sampled, and weighed. It is then cleansed and enhanced to optimize results. These steps may need to be performed multiple times in order to make data truly ready for the modeling tool.

 Modeling

Once information has been prepared, various modeling techniques should be selected and applied, and their parameters calibrated to optimal values. Choice of the modeling technique is determined by the underlying data characteristics or by the desired form of the model for scoring. In other words, some techniques may explain the underlying patterns in data better than others, and therefore, the outcomes of various modeling methods must be compared. A decision tree would also be used if it were deemed important to have a set of rules as the scoring model, which is very easy to interpret. Several techniques can be applied to the same scenario to produce results from multiple perspectives.

 Evaluation

Thorough assessments should be conducted from two unique perspectives: a technical/data approach often performed by statisticians, and a business approach, which gathers feedback from the business issue owners and end users. This often leads to changes in the model; but while the technical/data evaluation is important, it should not be so stringent that it significantly delays implementation and use of the model. The model’s business value should be the primary test.

 Deployment

Deployment, the final step, can mean one of two things: the generation of a single report for analysis, or the implementation of a repeatable data mining or scoring application. The goal here is to create a reusable application that can be used to generate predictions for large volumes of current data. The results are then distributed to front-line workers; in a format they are comfortable with – reports, dashboards, maps, or graphics – to enable proactive decision-making.

Avoiding common worst practices and adopting best ones, are the key to successfully implementing and using predictive analytics. By knowing what pitfalls to avoid, and what important steps need to be taken, companies can accelerate implementation, maximize user adoption, and realize substantial ROI.

About the Author

Shaughn is an industry analyst for business intelligence. For over ten years, he has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Shaughn is the channel expert for BI for the small and Mid-Market segments at ZSL and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. He can be reached at shaughnk@zslinc.com. And please visit Shaughn’s blog atzslbiservices.wordpress.com

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Worst Practices while deploying a Predictive Model

23 Jul

With the inception of predictive analytics the reactive decision making has been proven unsuccessful. Organizations have started to take more proactive approach by using predictive analytics to make critical decisions to uncover a problem or an opportunity. Predictive analytics not only enables the organization to determine the forecast of the problems and opportunities but also keeping the bad alternatives futures from happening. Hence companies can foresee their problems well in advance and neutralize them at a preliminary level. According to research from IDC, organizations using predictive analytics solutions generate an average return on investment of 145 percent. Regrettably, many companies don’t implement it correctly and fail to achieve these desired results

Common Worst Practices while deploying a Predictive Model

As beneficial as predictive analytics can be to an organization, implementation and deployment projects often fall apart or fail to get underway due to common poor practices, procedures, and decisions, such as:

  •  Failing to focus on a specific business initiative that predictive analytics can enhance
  • Ignoring crucial steps, such as data preparation and access, or deployment of results
  • Spending too much time evaluating models
  • Investing in tools that yield little or no returns
  • Failing to operationalize findings

Failing to Focus on a Specific Business Initiative

Mostly, companies begin building their predictive application with loose goals in mind and trying to discover something critical that they don’t know. Substantially they end up in trying various analytical models and forces developers into a never-ending cycle of definition, evaluation, and fine-tuning. The best approach and successful predictive analytics endeavor for an organization is to define the project objectives and requirements that will satisfy their business needs. Predictive analytics will be more effective when it is used to identify expected cases and to apply insight from specific patterns and trends existing in the data to these new cases.

Ignoring Critical Steps

One of the frequently confronted failures in deploying predictive analytics is ignoring critical steps. Many companies while deploying the predictive model many organizations take major efforts to look only at the important steps and often ignores the data preparation and access process. In reality, this should be the activity to which the most effort is devoted. In fact, data preparation typically accounts for approximately 60 to 80 percent of the cost of a predictive modeling initiative.

Spending Too Much Time on Model Evaluation

Predictive models must be evaluated to determine how accurately they predict patterns. Primarily, they must be measured from a data perspective and then they must be assessed from a business perspective to ensure they will meet end-user expectations and requirements. Accuracy comes at a cost, and companies must decide how precise they need their models to be. Companies often tend to over-evaluate. They add new variables to the models to increase their accuracy, which often requires rebuilding. They test and retest the models endlessly, spending tremendous amounts of time making continuous refinements because they are not quite perfect. This delays deployment, and prevents the organization from recognizing the substantial advantages that predictive analytics can offer.

There is a tradeoff to be made between time to market, usefulness, and accuracy. Companies must sacrifice some precision in order to accelerate deployment. Or they must halt implementation and rollout – and delay the realization of benefits – to achieve higher levels of accuracy. The truth is, if a model is better than the current approach to forward-looking decision-making (and it likely is), then it should be considered ready for deployment. No model will ever be perfect, because shifting business strategies and evolving end-user needs require continuous modifications.

Investing Heavily in Analytic Tools With Little or No Return

There are various common mistakes made when it comes to investing in predictive analytics tools. Companies frequently end up in buying expensive, complex analytical tools that is way too sophisticated for their needs. These solutions not only come with very high price tags, but also they are typically hard to deploy and difficult to use by anyone other than statisticians and experienced analysts. As a result, they likely contain features and functions that will never be used. All of these factors will significantly lessen the ROI of an organization.

Failing to Operationalize

 For predictive analytics to succeed, it must be embedded into applications that are leveraged whenever users need to make decisions. If an application is not built and deployed, the effort devoted to creating a model will do nothing to enhance forward-looking decision-making. The results will remain in a document that few people will refer to in support of their daily activities. However, when a model is incorporated into a dashboard or reporting environment, the results will be readily accessible to end users, whenever they need them. This will help to create an analytics-driven culture across the entire business.

How to avoiding Worst Practices

The worst practices we have highlighted don’t have to derail a predictive analytics initiative. In fact, they can all be easily avoided by:

Driving ROI

When planning a predictive application, companies must consider total cost of ownership and anticipated return, to ensure that maximum value is achieved.

Focusing on Bottom-Line Initiatives

Create models that will provide forward-looking intelligence to help solve specific problems (i.e., minimizing customer churn by uncovering the factors that contribute to it) or help to achieve certain goals (i.e., increasing up-sell and cross-sell revenue by understanding what new products customers are most likely to buy).

 Preparing Data

Guarantee the most accurate possible results by ensuring that disparate data is easily and properly accessed and cleansed before the models are created and applied.

 Evaluate the Model, Without Over-Evaluating

The model must be tested to ensure that it provides better decision-making capabilities over current analysis methods. But over-evaluation can delay deployment and hinder ROI. It simply needs to be assessed until it is determined that it will provide value. At that point, it can be implemented. The statistical properties of the finished model are secondary to the value it brings to the business.

 Deploying the Results

The insight provided by predictive analysis efforts must be shared with key stakeholders across and beyond the organization. For example, a bank that has predicted which customers are most likely to churn should disseminate that information to all those who interact with those clients, including call center staff and branch personnel. That way, everyone can contribute to correcting the problem and ensure that countermeasures are being implemented.

 We will see how to adopt best practices and the key to successfully implementing and using predictive analytics in the next article

About the Author

Shaughn is an industry analyst for business intelligence. For over ten years, he has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Shaughn is the channel expert for BI for the small and Mid-Market segments at ZSL and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. He can be reached at shaughnk@zslinc.com. And please visit Shaughn’s blog at zslbiservices.wordpress.com

BI in the Cloud

3 Apr

Business intelligence (BI) industry is experiencing a profound effect since the inception of Cloud computing. The exponential growth in the use of amorphous data by companies, including big data leads many decision-makers to grapple with the most efficient ways to analyze real-time data quickly and effectively. These intensifying data demands are just one of the reasons that predict the developing market interest in cloud-based analytics. While Gartner forecasts the global business intelligence (BI) market to grow 9.7% this year to $10.8 billion, business analytics software-as-a-service (SaaS) is anticipated to spring up three times faster than the total business analytics software market.

BI facilitates companies to analyze data and turn it into valuable business information. As more data and applications migrate into the cloud, numerous new data sources are being created. Cloud-based BI and analytics offer companies and business users multiple benefits. Businesses of all sizes can leverage vast computing and storage resources in the cloud without having to invest in expanding their existing IT footprints or IT support staffs. BI providers are adapting their tools to this new reality, and successful companies must now evaluate and act upon this opportunity. In addition, cloud-based BI and analytics provide business leaders opportunities to quickly gather and act on granular insights from a mix of structured and unstructured data.

Cloud-based BI and analytics tools also relieve business users from trusting on their employees to generate reports or create dashboards for them, thus facilitating decision-makers to act quickly on rapidly changing market conditions. Thus enabling organizational leaders analyze the sales pipeline, monitor the results of marketing campaigns, or trends involving individual line-item expenses and releasing other departments/employees to focus on other activities.

The availability of cloud-based BI and analytics helps to have a greater collaboration and teamwork between decision-makers, including those who might be geographically separated. For instance, users of a particular BI tool can embed dashboards within its dedicated platform via a stream so that followers of that stream can then obtain and share real-time information. Hence provides business users to easily share ideas and insights with one another

These capabilities provide line-of-business users the ability to promptly and easily make informed business decisions across all levels of the organization.
Benefits of cloud BI

  • Investment: Without any upfront cost, hardware and software systems can be managed remotely by the vendors or the services providers
  • Opex Vs Capex: The Money that has been invested by the business people are expense incurred to create future benefit i.e. acquisition of assets that will have a useful life beyond the tax year
  • Elasticity in terms of volume, usage, and price.
  • Less reliance on internal IT resources.
  • Scalability: Even the smallest businesses can have the ability to tailor a BI system to fit their needs and improve their businesses.
  • Cost: Instead of buying complete software or full license, a Cloud-based system provide companies a subscription-based model that ensure companies paying only for their usage.

It is believed that 50% to 80% of all BI applications fall or can fall under the above criteria and therefore SaaS cloud BI will be a huge market. So if you are planning for a Cloud BI, then you have taken an apt decision at your level.
To know more on ZSL’s cloud BI services and solutions, Contact Shaughn Knight.

 

 

Avoiding your frustrations in BI

29 Mar

I cannot say for certain that every; in fact, most organizations that endeavors business intelligence (BI) sees a demonstrable business benefit from it. They find it unmanageable, more perplexed and more pricy than they expected and are unable to quantify the business benefits gained from it. Thus, Organizations has been investing in or working on BI for some period of time, are only partially satisfied with it and are frustrated more with the implementations.

So what should an Organization do to avoid these frustrations?

Let us see this one by one.

Missing Strategy:

Forgetting to create a strategy before BI implementation is one of the common mistake that many organizations do before investing into it. The company must know what is the exact intention of implementing a BI in their organization, why are they implementing it, how a BI will drive their organization towards business benefits, ROI of the Organization etc.

Most of the companies fail to do these ground level researches within their organization. The management staffs involved in approving the budget is also not willing to do so. The reason may be they either don’t understand the benefits of a BI or they expect some else to do the ground level research and they will use it. This is the core factor that causes the downfall of BI in an organization.

Business intelligence is just like any other business improvement technique: there must be a strategy/plan/roadmap that describes exactly what it will do, how the organization must change to leverage it, and specifically how having it will drive positive business results.

Prior Preparations for a BI initiative:

 Before you plan for a new BI initiative, or looking to revamp a stalled BI program you need to make sure that your time, effort and money are not wasted. In order to foresee a successful implementation companies should have a track on necessary pre-conditions to achieve better outcomes.

What are these pre-conditions?

Pre-conditions are nothing but few prior preparations that you need to do before identifying a BI initiative

  • Make sure your quality of data as the quality of the information is directly proportional to the quality of the data that you provide
  • Make sure if you have a proper framework in place for managing and prioritizing your BI investments
  • Make sure your employees or your vendor has necessary BI skill sets to use the technical methods in BI. Especially when you consider executives with less computer knowledge, they experience a very tough time with BI challenges. So executives must be aware of the challenges they might come across during BI initiative.
  • Ensure your organization is aware of all the change management challenges in BI. In other words, it basically is a thought process; any time we’re asking someone to change the way they think about a problem or business situation, change management is a critical element They also need to identify and manage the risk involved with a BI initiative i.e. risks must be predicted, discerned and mitigate prior challenging your organization.

If your organization is capable to handle these pre-conditions, then you have crossed 50% of the frustrations in implementing a BI solution.

Lost Directions:

Assume that you are travelling on a road to success, at a point there are several roads diverging in different directions. What will you do, if you do not know the exact direction to success? obviously you will end up it losing your direction. The similar scenario happens in BI too. Organizations having multiple data warehouses, data repositories, BI environments, multiple BI tool sets, and multiple user communities lose directions while accomplishing BI.  The risk involved in such an approach leads to duplicated cost/effort and Incremental technical complexity and cost from multiple similar technologies in use. All those multiples are not necessarily bad, but they should not indicate an undiscriminating approach to BI.  It is a best approach to have the initial venture into BI with best guided by a BI strategy/roadmap and a BI data architecture plan.

Improper Execution

BI applications are not easy to use. It involves lots of user knowledge on tools and data structures and lots of manual effort. It takes too long for the BI team to complete projects or even to fulfill requests for minor enhancements/modifications or new reports and dashboards.  Your company may have well-articulated requirements, a sound BI strategy, and a good set of tools, but might not be good at designing, building, maintaining, and supporting BI applications.  The consequences would eventually leads to your BI applications that run slowly, break frequently, are hard to use, deliver uncertain results, take longer to build and cost more than they should. These factors indicate that your company’s BI execution is unconventional.

A Successful BI execution requires the following capabilities

  • Adequate financial support from the management of an Organization
  • Employees or external vendors  should have essential BI skill and complete involvement in the BI projects
  • Use of a BI-specific development methodology
  • Project management and change management skills and discipline

BI Impact

This is a common scenario faced by the organizations. They have invested, have one or more BI applications in hand, but they are unclear about the impact. The actual reasons are

The decision makers of the organization are not capturing the business value of their BI investments, limited usage of the BI that has been deployed and Key decision-makers in the business do not use information from the existing BI system. These reasons proportionally affect the additional funding for BI and increase the number of unsatisfied users in an organization who are to be the key decision makers. To overcome this scenario companies have to gather little information within their departments.

  • Who are the major users in the company
  • What and how the information from a BI tool will be utilized
  • Which KPIs or strategic objectives will be advanced by it

Just by doing this little research you can easily overcome the related challenges and feel the BI impact within your organization.

The above discussed points may help you to overcome your frustrations and at the same time all these frustrations have proven solutions through successful implementation history by companies like ZSL. However, BI can be challenging to do well, but the potential business benefits make it worth the effort.

Would you like to know our successful implementation?

Would you like to see our happy customers who have come out of these frustrations?

Would you like to see a demo on our Business Intelligence and Datawarehousing (BI/DW) services

Email me your interests Shaughn Knight, AVP – Business Development and Inside Sales Operations.

SmartPrise BI for Healthcare, Banking, Finance, Telecom, Pharma & Life Sciences, Travel & Logistics, Wholesale & Retail

26 Mar

ZSL’s SmartPrise BI® is the complete end-to-end Enterprise Intelligence solution for small-to-mid enterprises, which enables them to monitor, report and analyze their business performance effectively. The solution enables the enterprises to transform data into business-critical information that helps them to scale as well strategically plan to improve their business performance. SmartPrise BI Mobile for Health is developed based on Cognos, SSRIS, Business Objects, Spotfire, Xcelsius and can fit with any leading ERP & CRM applications that include Microsoft Dynamics, Infor, SAP & Oracle.

SmartPrise BI® suite provides enterprises accurate, consistent and clear data in the form of reports, scorecards and dashboards that acts as a key information for decision and policy makers to give consents, frame policies and guidelines and allocate funds for different planned and ongoing activities across the organization. The suite is addressed to meet the intelligence analytics needs of wide range of industry verticals which includes Healthcare, Banking, Finance, Telecom, Pharma & Life Sciences, Travel & Logistics, Wholesale & Retail.

ZSL’s SmartPrise BI® suite includes:

  • Enterprise Data Warehousing: ZSL follows on industry best practices in developing and delivering data warehousing platforms that span the business and analytical data warehouse needs of the enterprises.
  • Enterprise Data Management: the platform brings together the various data sources through integration between various application systems such as CRM, ERP, Legacy and third-party applications and is organized and managed through well-built service oriented data architecture.
  • Enterprise Data Delivery: ZSL’s SmartPrise BI® suite is flexible and agile that enables the users to have data drilled to any nuances and is delivered in any preferred format such as pie-charts, graphs, dashboards, reports and more. The reports can also be posted into a portal, emailed to users or allow for web-based access.

SmartPrise BI® suite is available for multiple deployment models – On Demand, SaaS & Cloud.

SmartPrise BI® Mobile
The mobile enablement of SmartPrise BI® suite enables the end users to access the mission critical reports over their smart phones and tablets such as iPhones, iPad, Blackberry, Windows Mobile & Brew devices.

Key Benefits

  • Key information that analyses your present and past business performance and guides the decision makers to frame and consent policies and processes suitable to the organization at time of need
  • Serves as a yardstick to measure your employee productivity, operation efficiency and business performance as a result of your new policies and programs
  • Proven and successful methodologies to extract, manage and deliver data
  • Centrally managed data, business hierarchies, rules and calculations to eliminate data silos and inconsistencies throughout the organization
  • Proven and cost-effective BI&DW methodologies built on prudent architecture powered by IBM Cognos which offers seamless integration between various data sources and compatible for SOA, Web 2.0 and BPM capabilities
  • Easy to interpret and analyze data using reports, charts, scorecards and dashboards in preferred formats that has a rich new look & feel and instantly accessible over web and mobile devices
  • Faster implementation services and simple configuration procedures saving 30% of your time and money involved in implementation and roll-out
  • Flexible, role-based security model to protect the reports and reporting resources appropriately and also includes extensible interfaces for integrating other security models if desired

Is your organization looking for a BI tool? Have your organization chosen the right one?

Email me your interests Shaughn Knight, AVP – Business Development and Inside Sales Operations.

Why should you go for a BI when you have an ERP in hand!!!!!!

20 Mar

When you rewind the IT history a few years back, most industries preferred ERP as a significant tool to generate their organizational data. With the inception of sophisticated BI solutions and implementations the hype on ERP phenomenally reduced as ERP wasn’t generating the required information to run a business. While BI was significantly able to generate data and information alongside which was sufficient enough for companies to minimize the reporting structure, taking right decisions for their business etc., Hence industries started giving enormous importance to the BI solutions leaving less importance to ERP and CRM

Fairly speaking, ERP is the true backbone of any company or business. ERP was designed for efficient data entry and storage to provide much-needed capabilities, such as management of financial, product, human capital, purchasing and other transactional data within one environment. Certainly they are not the best tool for reporting as the information delivered by an ERP is at a very detail level. If you take any organizations and their top-level executives, they wouldn’t like to see business users essaying them the reports, they wanted it to be short and simple and easy to understand. So to ease the reporting structure and to make right and accurate decisions most organizations prefer a BI solution.

BI on the other hand provides robust access to different reports, dashboards and balanced scorecards facilitating business users in streamlining their business operations with accurate decisions. In simplest term just a facilitator for reporting and hence it is the decision-making solution for businesses so that they can cut costs and hence increase profits.

For any normal business, a need for an ERP, CRM or SCM system comes first and then they start thinking about using a BI solution. What if these organizations are given a chance to use BI along with their existing ERP or CRM tool? Is it possible for these Organizations?

The answer is SmartPrise BI. ZSL, Inc with an intention to showcase their BI capabilities to all small and midsized organization emerges with an exclusive, sophisticated and cost-effective BI add-on for CRM and ERP. Now the business users of small and midsized organization have to just integrate this add-on to their existing ERP/CRM system and get the complete benefit of that of a BI tool.

Salient Features:

  • Cost effective implementation
  • Filter, sort and analyze data.
  • Formulate ad hoc, predefined reports and templates.
  • Provide drag and drop capabilities.
  • Produce drillable charts and graphs.
  • Analytical features like ranking, filtering, sorting, group by etc…
  • Dashboards with KPI reports
  • Interval Grouping on date field
  • Distribution reports with XML Publishing
  • Export reports in different formats (PDF, XLS, TXT, HTML etc.)

In case of any further customization required to this add-on, based on the customer requirement, ZSL can provide the customization.

Is your organization looking for a vendor? Have your organization chosen the right tool?

Email me your interests Shaughn Knight, AVP – Business Development and Inside Sales Operations.

“If you wish to avail my blog article please click the follow button at the bottom of this blog”

How Healthcare Organizations gets profited by BI?

7 Mar

How Healthcare Organizations gets benefited by BII have seen many hospital administrators who have been using the Healthcare analytics for creating quality reports and capture better revenue. These administrators are working in teams with their IT department and other hospital staff in determining what to spend, and where to spend it, as BI takes on a greater role in health care. Like many industries, health care seeks ways to improve efficiency and reduce costs. Hospitals are turning to analytical tools to help accomplish these goals and to create better clinical outcomes for patients. On the other hand hospitals equally are just getting started or are planning to implement data analytics.

Healthcare industry as such is full of modern challenges in data quality and its distribution and hence indeed, BI is so important to this industry for not only analyzing the data but also to know how the results of these analyses are related to everyday business decision.

 Issues faced by Healthcare Industry with respect to BI

  • Analyses that often leads to organizational confusions and loss of business opportunities due to inconsistent data definitions across multiple and heterogeneous data systems
  • Individual departments not receiving timely analytic support create their own internal databases and approaches, exacerbating the issue across healthcare systems. These shadow systems generate unnecessary costs and the outputs lack data quality, security, and consistency, thus the need for a dependable source of experienced support.
  • There is a no common understanding or agreement on definitions or consistent data flow. Different data bases display information based on different sets of qualification criteria, highlighting the fact that there is no consistent definition or standard for how information is extracted, viewed and analyzed.
  • Lack of common agreement on the value of each data field because each data element is defined and used differently in each area of the organization
  • Inconsistent outputs and highly variable interpretations due to data reconciliation is another primary issue. There is no central group chartered to pull the data when requested. Planning, Finance, Quality, Marketing, Med Staff, or others may be contacted
  • The experience set of the analysts that receive business requests and pull the data do not have the deep insight required to interpret business drivers behind the data requests.

BI and Lowered Healthcare Cost

  • Helps you to meet the rising demand for achieving competitive advantage
  • Healthcare organizations improve patient care by driving better decision making throughout the organization.
  • Helps you to have a clear insight into clinical data and hence clinical decisions will be evidence based.
  • Helps to supervise and endeavor over inflating costs and operational processes, and increase the quality of care and the financial health of the organization.
  • Helps to improve patient safety and clinical quality by providing reports and analytics to give insights into business operations.

Use of Key Performance Indicators (KPIs) in Healthcare

With the use of the KPIs, Hospital Performance can be monitored and analyzed. KPIs on accounts receivable, operating profit and expenditure involved in development of latest medicines and amenities help in evaluating the fiscal advancement and performance of the health care facilities. It helps to mine patient data for clinical patterns and treatment protocols and track the cost of supplies to the cost of procedures. It helps to generate clinical and service line reporting and analyze potential contracts while negotiating with payers. Thus using KPIs BI permits management to have a luculent idea of events occurring in a hospital or in multiple group hospitals.

Improved Business Intelligence efficiency on Workflow Automation

  • To avoid repetition of errors in medical field, workflow management must have integrity.
  • Supreme compliance must be attained to a great extent so that progress and good management will go hand in hand.
  • This will bring to a successful conclusion that should be confirmed.
  • It also assists flexibility in the business process by advancing with customer needs and competitions.
  • Medical Assistants are heavily involved in making sure data is properly recorded and utilized.
  • Patients would ensure their confidence in a medical institution when they see that they have the right medical assistants who can give them satisfaction.

Is your organization looking for a BI vendor? Have your organization chosen the right tool? Just a BI add-on to be integrated to your existing EHR?

Email me your interests Shaughn Knight, AVP – Business Development and Inside Sales Operations.

“If you wish to avail my blog article please click the follow button at the bottom of this blog”

Business Intelligence Is More Important Than Ever

28 Feb

From CRM to Supply chain management there are around 200 and above categories of software that helps companies in back office and front office operations which are considered to be an essential segment for a proper business management. Most companies require both these segments and the one particular tool that’s becoming more indispensable is the business intelligence and data warehousing (BI/DW) software enabling more effective business process management (BPM) by providing comprehensive analytics that are primal to sustain and support profitability and growth of an organization.

What is Business Intelligence (BI)?

Business intelligence (BI) can be interpreted as a broad category of applications and technologies for accumulating, stacking, analyzing, and supplying access to data to help organizations/users make better business decisions. BI/DW applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining. Thus leveraging data from various systems, applications, and databases, business intelligence software users can make the best-informed decisions more efficiently.

Key Benefits of Business Intelligence

Companies of all sizes can benefit from business intelligence software, and it is important to consider the fact that companies acting without it are in a sense running on gut feelings. Every company needs to think on its feet, and leverage its enterprise data for the greatest advantage. After all, this data is essential for conducting and understanding mission-critical activities, operational patterns, and industry trends. Every company has varying needs, and there are a wide variety of benefits within each company;

  1. No More Guesstimate: Far too often, business data of organizations lacked unified structure and as a result guestimate was the best weapon for executives to make decisions while attempting to direct their companies into the future. Business intelligence can render precise historical data, forecasting and trending, real-time updates, synthesis between departmental data warehouse and even predictive ‘what if?’ analysis, eliminating the need to guesstimate.
  2. Mobility: Availability of Mobile based business intelligence applications provide walkthroughs for users/executives to access key business metrics, reports and dashboards on iPhone, iPad, Droid or BlackBerry, giving sales and marketing people access to critical business information on the go.
  3. Retain Your Customers: One of the great benefits of BI/DW tool is that it permits companies to attain visibility into what customers are buying or what they need , providing them the ability to gain additional profit and retain valuable customers.
  4. Selling Opportunities: BI/DW software permits organizations to leverage customer data to build, refine and modify predictive models that help sales executives to up-sell and cross-sell products at appropriate customer touch points
  5. Improve Efficiency: Apparent amount of time are wasted by companies in hunting data within their departments to find the information they want and process as a report to take a single decision. With the influence of business intelligence software, however, all the information is centralized and can be viewed in the form of dashboard or converted into a report, saving enormous amounts of time and improving efficiencies.
  6. Greater lucrativeness: Business intelligence software can provide users greater insight into manufacturing costs and the ability to line up production on the go for greater profitability
  7. Greater Inventory Management: BI helps organization to order the right amount of inventory at the right time so that customers receive their products when they need, thus avoiding over stocking
  8. Business Insight: Business Intelligence has been very successful at explicating the complete insight of your business over a period of time. The tool can provide you insights on how many units were sold, through which store, in which geography, or by which customer segment. Hence, these issues are now well understood, and so the next generation of competitive advantage comes from analyzing unstructured content to understand how and why these things happen. From simple content-based metrics to sophisticated sentiment analysis, business intelligence software can provide a more complete view on customer and competitor experience and opportunities therein and help executives plan for the future.

What to Look for in a Business Intelligence Software Solution

While Analyzing BI/DW solutions for your business management needs, there are a few important features to look for.

  • It’s a best way to find a web-based program, and one that offers unlimited data access as on-demand solution will provide accessibility for more users, and will support scalability should the company need to add more users and run more reports in the future.
  • Not all business intelligence software is fully scalable, so choose one that addresses specific capacity needs.
  • It is also important that a BI/DW tool should support several output formats, from spreadsheets to PDF documents for producing reports and to run the reports.
  • Corporate data can be highly complex, and reporting tools should support that. In the same vein, the platform should offer predictive analysis, unlimited data access, and complex visualizations.
  • The visualization abilities should range from simple charts and graphs, to maps, matrices, and histograms depending on the mathematical or scientific reporting necessity

Business Intelligence Is More Important Than Ever – Why?

Apparently, adopting a BI/DW solution is a smart step for companies of all sizes and in all industries because of its analytical possibilities and it is quite likely that soon, companies not adopting one will have a difficult time surviving the competitive world. Businesses are currently collecting more data than has been amassed before, and as a result IT organizations are spending billions of dollars investing in data warehouses and business intelligence tools.

In addition, with the surge or Web 2.0 technology within enterprise software, business intelligence systems are beginning to provide real-time information. Minimizing reaction time between an event’s occurrence the subsequent business decision is the most important point of business management, and while business intelligence solutions have been able to predict outcomes before, the availability of real-time data will further decrease any latency periods.

As you can see, BI/DW Solutions are more than just a reporting tool, as through recent technological advancements it now offers highly evolved predictive abilities. So when looking for business intelligence software, outline your goals and choose the scalable solution that best fits your informational needs. The more complicated your data, the more complex the visualizations should be; likewise, don’t select a convoluted system for simple data. And one thing your selection should include, no matter what your data needs, is real-time reporting and analysis.

Is your organization looking for a vendor? Have your organization chosen the right tool?

Email me your interests Shaughn Knight, AVP – Business Development and Inside Sales Operations.

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Penning the Use of Big Data Analytic in your Organization

23 Feb

Companies have been stacking and analyzing huge volumes of data since the inception of the data warehousing drift in the early 1990s. From terabytes the rate of growth in data volumes has reached to petabytes and continues to escalate as organizations seek to stack and analyze greater levels of transaction details, as well as Web- and machine-generated data, to gain a better understanding of customer behavior and drivers.

Why Big Data?

There has been a lot of talk about “big data” in the past year, what is big data? Why are we are talking about big data today?

Big data is nothing but the term that describes the voluminous amount of unstructured and semi-structured data that are created by an Organization i.e., data that would take too much time and cost too much money to load into a relational database for analysis.

There are several factors influencing to talk about Big Data

Varying data types: Until few years ago it was easy for organizations to capture the data that was transactional in nature and numeric to fit easily into rows and columns of a relational database. But today, the growth in data has predominantly increased from websites and social media contents that has made organizations find it difficult to structure the data

Advanced Hardware Technologies:  The advancement in price performance and technology of the hardware materials has finally made it easy to store and analyze the huge volumes of data at affordable prices. Vendors exploit the technology advancement by developing high speed analytical platforms to accelerate large volumes of data, while the open source community has developed Hadoop, a distributed file management system designed to capture, store and analyze large volumes of Web log data, among other things.

Outsourcing Big Data: Because of the complexity and cost of storing and analyzing Web traffic data, most organizations traditionally outsourced these functions to third-party service bureaus like Zylog. As the size and importance of data analysis increased, many are now eager to outsource this data to gain greater insights about customers. At the same time, virtualization technology is beginning to make it attractive for organizations to consider moving large-scale data processing outside their data center walls to private hosted networks or public clouds.

New and Exciting:. The biggest reason for the popularity of the term big data is that Web and application developers have discovered the value of building new data-intensive applications. To application developers, big data is new and exciting.

Use of Big Data Analytic

Now that we understand the business context for analytical platforms, an analytical platform is a data management system optimized for query processing and analytic that provides superior price-performance and availability compared with general purpose database management systems.

According to a survey, 72% of the organizations had purchased or implemented an analytical database whereas 46% have no plans to do so, 42% are exploring the idea and just 12% are currently evaluating vendors. On the whole, about 75% of the organization will have an analytical platform in the near future.

Implement a new BI architecture

The BI architecture of the future integrates traditional data warehousing technologies to handle detailed transactional data and file-based and non-relational systems to manage unstructured and semi-structured data. The key is to incorporate these systems into an amalgamated architecture that enables casual and power users to query report and analyze any type of data in a comparatively unseamed manner. This integrated information access is the hallmark of the next generation BI architecture. More immediately, companies are using Hadoop to preprocess unstructured data so that it can be loaded and integrated with other corporate data for reporting and analysis. This allows BI and ETL users to use familiar tools to query and analyze data.

Implement analytical platforms that meet business and technical requirements

Today, organizations implement analytical platforms for various reasons. For example, analytical appliances are fast to deploy and easy to maintain and make good replacements for Microsoft SQL Server or Oracle data warehouses that have run out of gas and are ideal as freestanding data marts that offload complex queries from large, maxed-out data warehousing hubs. Analytical databases, as software-only solutions, run on a variety of hardware platforms and are good for organizations that want to tune database performance for specific workloads or run the RDBMS software on a virtualized private cloud. Analytical services are great for development, test and prototyping applications as well as for organizations that don’t have an IT department or want to outsource data center operations or get up and running very quickly. File-based analytical systems and non-relational databases are ideal for processing large volumes of Web traffic and other log-based or machine-generated data. Organizations need to carefully evaluate the type and capabilities of the analytical platform they need before purchasing and deploying a system.

Choosing a Vendor

When an organization is implementing or purchasing an Analytical database from a vendor they need to keep in mind the 4 important criteria before choosing them

  • Whether the selected vendor will meet your requirement?
  • How successful the vendors are in implementing an analytical platform?
  • Whether the vendor is liked and trusted by other organizations?
  • How efficient is their support and services?

Interestingly the pricing, customer references and vendor incumbency should be considered as minimum qualifying criteria.

A vendor should have the ability to meet more of a customer’s requirement. This is because many analytical platforms implemented by the startup vendors are new to the market and heavily priced and customers might not know the exact value of the product. So Organizations should make sure that the product they are planning to implement meets their current and future requirement of the DW architecture. Also, Organization should look for the quality and responsiveness of the vendor to meet their needs.

How can your organization choose the right vendor? How can you get benefited by Big Data Analytic?

Email me your interests Shaughn Knight, AVP – Business Development and Inside Sales Operations.

BI/DW for Banking, Financial Services and Insurance (BFSI)

16 Feb

BFSI is one of the complex and sophisticated industries in the world as these industries are subjected to generate substantial amount of data. Indeed, companies entering these markets confront several difficulties to stay abreast of changes in the market, changes within competitors, and changes in customer and changes in government and regulatory bodies. In order to compete with others better modes of attraction and retention of customers to be identified and dependable strategies to be deployed. The enterprise must also achieve an integrated view of data across all systems and data sources in order to provide up-to-date data for analysis and decisions. At the same time, these enterprises must focus on a secure transaction environment to ensure compliance and protect confidential and private data. To be successful in this environment, an enterprise must establish and monitor appropriate key performance indicators (KPIs) and metrics, identify and support profitable customers, improve operations at the grass-roots level and achieve true business intelligence to understand and improve portfolio performance and predict and forecast results based on trends and business patterns.

BI/DW for BFSI

Business intelligence (BI) not only can support, grow and ensure the success of the BFSI companies but also its users and other professionals involved with it.

Banking:

  • BI facilitates the banking organization to change the financial performance and allows the organization to set up and meet goals and track progress using a huge volume of information, integrated from numerous enterprise systems and sources. Banking professionals and stakeholders can monitor their financial strength and create and share reports to abide by the government and industry regulations
  • BI helps to excel the banks in customer service and meliorate customer acquisition with targeted marketing and market research. Users can distinguish the most profitable customers and analyze buying behavior and preferences
  • BI helps users to measure risk trends and forecast profitability with reliable data. Moreover it supports all banking requirements and analytical needs including credit analysis, regulatory compliance, operations management, loan monitoring, dealer loan analysis, demographics, customer profiling and many other factors of business success.

Financial services

  • BI enables the users to identify and focus on business factors that are in need of betterment or fine-tuning. Financial services companies can maximize operational efficiency and gain profit margins by establishing and cascading new strategy and monitoring operational performance and tactical performance, KPIs and metrics. Automated alerts enable the business user to customize information views and receive updates when a critical threshold has been crossed; thereby ensuring that corrective action is taken swiftly and decisively.
  • BI facilitates to analyze and manage centralized administration of all consumer information, with data integrated from several enterprise systems and sources. Users can create personalized dashboards to view this information in a convenient way can be easily understood by them. They can summarize data and drill down data to present results in charts, graphs, and gauges for presentations and analysis.. Users can analyze consumer credit and monitor activity to identify the most profitable markets, customers and demographics, and to mitigate default and risk.

Insurance

  • BI facilitates insurance companies to manage and analyze Sales, marketing and channel partners thereby enabling the business user to monitor information from various distribution channels and identify which customers are more profitable and forecast results with complete insight into which channels are the most cost efficient
  • BI helps in agent performance analysis to identify cost saving opportunities and increasing yield. Managers can promptly determine which agents have the highest customer retention rates and monitor actual results against plan
  • Claims results can be monitored and new trends and strategies can be deployed to detect fraud and risk causing factors. Users can establish automated alerts to notify them of specific issues so that the user can take quick execution to resolve problems or determine the root cause before missing the target
  • Manage and Analyze actuarial and risk factors by  evaluating new trends, identifies areas and markets where risk is increasing and enables swift risk analysis with robust reporting, charts, graphs, gauges, alerts and other tools to support decision-making and extenuate risk.
  • BI supports all insurance requirements and analytical needs including credit analysis, regulatory compliance, operations management, policy monitoring, underwriter and loan analysis, demographics, customer profiling, claims analysis and many other factors of business success.

Advantages of BI/DW services and solutions in BFSI industry

  • Maintain and Ensure to meet the regulatory, governmental and industry compliance by balancing the risk involved between business and growth
  • Produce user-friendly  Reports on performance, customer demographics and other factors through dashboards and KPIs to the management
  • Analyze and act on customer business intelligence
  • Provide a bird’s view structure of risk, customer profiles, products, services, regions, sales, marketing and other information and indeed, increase customer profitability, ameliorate customer service and cut down operating costs and risk
  • Fulfill user needs for diverse views and perspectives and provide custom support for individual roles, functions and team activities
  • Boost revenue  and Improve efficiency of the overall business process and increase the confidence of decisions making for the management
  • Increase value of investment with full insight into costs, performance and risk factors and Leverage investments in existing resources and infrastructure by integrating data from existing sources
  • Assist in cost control, resource allocation and operational management
  • Ensure accurate, timely decision-making with granular, enterprise-wide information, graphical displays and personalized dashboards and alerts to provide up-to-date information and simple analysis
  • Identify and take corrective measures for inefficiencies, process issues, and under performance using powerful key performance indicators (KPIs) and metrics
  • Calculate and adapt pricing and strategies based on accurate forecasts and trends
  • Align performance with strategy and objectives and enforce accountability
  • Accomplish complete insight into financial and operational results, claims adjustment and management, overhead costs and fraud and risk management

Is your organization into BFSI ? How can your organization adopt successful business intelligence implementation? Email your interest to Shaughn Knight, AVP – Business Development and Inside Sales Operations.