Tag Archives: BI & DW Services

Big Data Utilization

17 Jul

If big data is such a popular technology, why haven’t more enterprises adopted it? What benefits can BI professionals expect, and what best practices can make your big data project a success? Where will big data be in five years?

Business value of big data

Beyond providing the traditional data from the transactional systems, big data is capable of providing business insights that offer valuable perspectives from a B2C model and insights into B2B model. Therefore one can perform deeper contextual analytics by integrating big data and data warehouse (DWH) analytics into one platform, which was not possible earlier.

Utilization of big data analytics

Big data enables BI professionals to leverage expanded analytics and create round the clock view by providing access to consumable data. Let’s see how big data analytics are being utilized.

For CRM systems, you can create powerful bird eye view of customer opinions, wish lists, and customer response data from campaigns to evaluate true campaign potentiality. You can model and forecast customer behaviors by integrating data across call centers, blogs, forums, and social media platforms into deeper analytics. You will have the ability to position better call-center metrics for customer management or even produce an efficient micro-targeting and micro-segmentation model for new customer acquisition that can provide better response rates of acceptance.

If your organization deals with products and/or services, with big data analytics you can create powerful models for trends, behaviors, and markets, and you can solve research and intellection issues by leveraging tasks to a distributed group of people and embedding analytical results from your work. If you work in the utility industry, you can create predictive models of consumer markets by implementing technologies such as a smart grid. This would help organizations to create more revenue opportunities in advisory services and render better models for rate management.

Healthcare is another popular industry where utilization of big data analytics is exponentially high. For example, service providers can leverage big data to deploy Body Area Networks (an application of wearable computing devices that enable wireless communication between several miniaturized body sensor units (BSU) and a single body central unit (BCU) worn at the human body), helping lower patient costs while providing “patient-centric” services. Lowering costs and enabling efficiencies are critical goals for hospitals, nursing homes, and clinics. Another application of big data is to optimize clinical trials to prevent errors, reduce costs, and ensure compliance and ensure that regulatory requirements are met consistently. Although these analytics are partially fulfilled today, their expansion will enable proactive approaches rather than reactive ones.

With new technologies being evolved into the burgeoning BI market it’s possible to integrate any information into traditional platforms. These data points can be represented in analytics and visualization that can help any organization in any industry to improve their quality of services.

Difference between big data and a traditional analytics

With respect to knowing why more organizations are adopting big data analytics, we need to know the difference between traditional and big data analytics. Traditional analytics are based on structured data providing only the insights of an issue but often fall short in predictive and indicative analytics. Therefore, lack of near-real-time information and expanded information beyond structured data is unavailable. This is where big data analytics comes into existence enabling better analytical insights by integrating more voluminous data of varying complexity and timeliness into one structured output.

After integrating text, voice, streaming data, and unstructured data analytics into one model, we will be able to tackle the different aspects of related information into analytical models rendering potential, multi-dimensional metrics that can be leveraged with traditional analytics.

However, adoption of big data is less amusing than expected in traditional enterprises because current business models and goals don’t demand big data integration. Moreover, there is no perceived additional value offered by big data as to the organization. Hence, there is no clear business case articulated, and thus no business value calculated.

There are other suppressing factors. Lack of understanding of big data by the executives, which also brings processing complexities that create additional stress on IT teams (in terms of maintenance) and business teams (in terms of adoption and usage). In these times of financial constrains, IT teams obviate to implement yet another new system or technology.

Legitimately, there is going to be definitely a vulnerable change in the infrastructure for executing the current technologies in the future. It might take a long and complex period, but with guided navigation from concept to adoption, big data will continue the authority into the predictable future.

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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Self-Service BI & adapting Line of Business (LoB) Executives

19 Apr

Today, BI has sprouted more than just a technology as organization across the globe demand over more relevant and faster information to support decisions. Eventually data volumes are conservatively increasing 30% a year and enterprises find it difficult to handle these enhancing data sources that enrich their analytical systems. Best-in-Class companies are able to identify these needs and allocate appropriate IT resources to manage these back end data warehouse leaving only the functionality reports to be created by the Line of Business executives. However LoB executives are struggling to find a way to capture the appropriate insights from the information that are streamlined in and out of the organization.

Line-of-Business executives are entitled to make more agile decisions with the efficiently streamlined information. LoB Decision makers has no longer have an option to wait for the IT executives to generate their reports, they need to be abreast of the market sheers and quickly respond to the threats and opportunities they face. Hence they require self-service access to their analytical solutions to take right decision without the interference of the IT. Top performing companies have started implementing a comprehensive road map to develop their technically imperfect LoB executives into analytically inclined decision makers, spread Business Intelligence (BI) capability to more organizational functions, and drive significant internal and external business efficiencies as a result.

The self-service delivery model to BI is being adopted by many enterprises in order to create significant efficiencies for organizations and meet the growing need for business visibility and decision. The self-service approach allow

  • LoB executives to increase the curiosity towards their analytical thinking to learn more on the BI tool
  • Companies to extinguish IT intervention into the deployment and support of the BI tool
  • To create more analytical friendly environment wherein reports and dashboards can be created, applied to the data and distributed to the executives within the organization
  • LoB executives have the complete freedom to create their desired customized data that induce them to make more confident and perfect decisions that significantly leads to improved business performance.
  • LoB executives to drive value form traditionally untapped organizational data.

While speaking about the companies adapting to the self-service delivery model there are two main factors that are driving companies to generate a self-service environment for BI.

  1. Most enterprises are experiencing a growing need to create business visibility between top management and specific departmental levels i.e. In a business the view of a top management executive will be only at a strategic level and he/she might not be strong enough to understand the functional area similarly the LoB managers of an organization have a good understanding of what drives the business but strategically weak to take decisions. In order to improve the business visibility, companies therefore leverage the self-service environments.
  2. Most departments in an organization find themselves submerged with raw data about their business. While efforts have been made at the IT level to capture and manage this data, many have yet to leverage those efforts to deliver actionable insight to the department level.

These two factors are top of mind for companies looking to achieve self-service BI at the functional level.  Companies are looking for precise fact-based decision-making rather than decision via gut-feel. BI is generally designed to enable more fact-based decisions and most LoB executives share the overall desire to extenuate doubtfulness in how they run their business. Additionally, the need for self-service BI has shown to be born out of economic necessity. Many organizations have been forced to cut IT spending and therefore transfer not just the technical burden of BI to LoB executives themselves, but the financial obligation as well. Many of these cash-strapped companies are looking for creative new ways of delivering analytical capability, and a new direction being considered is outsourced BI model delivered via web-based Software as a Service (SaaS) interface.

For a more detailed discussion of BI deployment methods contact shaughnk@zsl.com

Business Intelligence implementation – How to guarantee your Success

16 Apr

Organizations often start their sophisticated Business Intelligence implementation with aspirational and competitive goals in an attempt to meet their business needs. However, they are ending up or restricted with department-levels due to the substantial business dimension that clutches around it.

The intention of organizations to implement a Business Intelligence solution is a mighty step, but how will you avoid these implementations getting restricted at department levels.

Mistakes during implementations

Organizations are most familiar with the mistakes that they do however they don’t realize them.

On time project delivery

Generally, Business intelligence and data warehousing implementations are large and complex, if enterprises don’t eventually define the scope of the work then the implementation becomes more complex, time consuming and cross the budget than predicted. Most companies fail to create the scope of the work and don’t realize the consequences out of this negligent behavior.

It is well interpreted that building an implementation plan would provide enterprises utilize the BI tools sooner and faster, make quicker decision making in terms of new functionality, gaining traction with smaller groups.

Strategic initiatives and Unavailability of depth reports

This interconnected factor to on time delivery is a tough balance to strike. Organizations need to approach with a strategy that would first measure the impact on the business and then technical foundation of new projects required to support the business within the subject area to deliver depth and rich reports. These strategic business initiatives on the basis of high business value that have low technical complexity to implement would help them to drive expected business value. When enterprises fail to approach these type of strategy and focus only on delivering subject areas and reports that can span across the organization they end up with departmental levels.

Troubling Support and Training

Business initiative must be followed by support and training activity as a part of the deliverable of the architecture phase. But most of the organization makes a mistake by forgetting to include the support and training cost as the part of BI initiative budget. Significantly an organization must provide technical training and support for a single user or a small group of users so that they can help the other users in knowledge transfers on how to design and build a BI repository or an ETL process, how to use the tool, how to manage the tool etc. These trained users become the nucleus of the design and developer skills in house for the data warehouse platform and BI toolset and first line of support for other users. This is one of the simplest of the initiatives that can be fixed, but after the initial project delivery this activity is being forgotten by the BI services implementer or the BI service vendor.

Lack of Internal communication

Another factor that affects the enterprise wide project is lack of internal communication within the organization. Business intelligence systems are contrasted to more of information gathering by their inter-departmental focus and their general overview towards business performance. They are also unique in their use of advanced technology and techniques to mine for data and to crunch that data in the most optimal manner. While a group in charge of market analysis might have a strong understanding of the particular sector of the market in which a business operates, their lack of the same detailed understanding about specific competitors and the inner management of the company make their information less useful. In a business intelligence model, all these various forms of business improvement should be tied together so that communication is quick and easy, and each segment should help to inform the other segments so their insights are even more valuable than they would be on their own.

Strategies that organizations must follow to avoid these mistakes

  • Ensure to begin your strategy with an insight towards Enterprise Data Warehouse that leads to high-level BI architecture
  • Understand various business process workflows of each department and gather the data required to support the high-level BI architecture.
  • Originate an implementation strategy that is important to deliver high business value and derive incremental deliverables to build your BI architecture
  • Develop strong working relationships with key business sponsors.
  • Educate a group of users with key technical knowledge and business and data analytics
  • Develop a strong communication barrier within the organization in order to gather information from different departments.
  • Take a release level approach to the incremental delivery of new (ETL and BI) functionality. Use strong (ETL and BI) tools and adopt common development standards; the ETL admin and the BI admin will be the central point for development standards. Pay attention to data profiling and data cleansing, and informally start to build master data. Don’t underestimate legacy data challenges, and don’t underestimate the challenges of unstructured data. Don’t underestimate training and support requirements, including the initial technical training, the initial business-user training, and ongoing training.

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.

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