Business leaders are often highly reliant on every single piece of their business information to make profitable business decisions and further refine corporate strategy. This is where business intelligence comes into the picture.
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Designed to help people make better business decisions and turn the business into a profitable establishment, business intelligence (BI) is a technology-driven process that involves the collection, integration, analysis, and presentation of business information through historical, current, and predictive views of business operations.
While, there are many industries have started embracing BI, let’s see where does it stand in the healthcare industry.
Business Intelligence and Healthcare
As the amount of information being generated is increasing exponentially, big data revolution has left many industries with repositories of information that they earlier had limited or no access to.
Industries are now looking into the prospects of moving to a data-driven establishment. The healthcare industry is also on the brink of transformation and according to a report by KLAS, over half of all health organisations are planning to buy a new and more advanced business intelligence system over the next three years.
Gartner reported that one of nine deadly mistakes in business operations improvement (BOI) in healthcare is the lack of BI strategy.
“Most vendors working in healthcare and other industries observe that healthcare has the most complex data of any industry (possibly excluding government intelligence efforts),” the report states.
The report also cited that organisations are not yet fully handling their data the way it should be tackled. The total absence of a well-documented BI strategy, Gartner added, is the biggest folly of the healthcare industry.
Forward thinking organisations like Harvard Medical School, St. Joseph Medical Centre, and Lincolnshire Partnership Foundation Trust realised the importance of business intelligence in analysing healthcare data and swiftly plunged into the world of BI solutions to better analyse the efficiency of their services.
In the light of such findings, it’s fair to say that bridging the distance between data created and data analysed is where the success of the healthcare industry lies.
The rapid proliferation of new technologies at lower costs with greater availability of patient data has given birth to an immense opportunity for healthcare organisations to improve their efficiency.
Every healthcare business solution comes packed with a myriad of business benefits for organisations to better handle their data and further refine their health strategy.
So, let’s take a quick look at the main benefits of business intelligence in the healthcare industry.
How BI Can Help the Healthcare Industry
There are categories of data that any healthcare organisation should ideally be interested in, such as financial, operational, and clinical.
In order to make better decisions, healthcare BI could be applied to organise this data into a form that can easily be accessed, readable, comprehensive, and analysable.
Single Point Access to Patient Data
A single point access to patient data makes their confidential information easier to protect, and improves privacy.
It also becomes easier for healthcare professionals to retrieve a patient’s data and create an assortment of reports and analysis based on the historical data stored in their system.
Improved Operational Efficiency and Better Visibility
Such consolidation of data and easy access to information not only results in improved operational efficiency, but it also increases revenue and reduces the overall cost of healthcare.
It does so by improving the visibility of their operations, helping distinguish the highly profitable ones from the underutilised services.
By establishing a concrete ground for evidence-based decision making, one that is essential for the healthcare industry, BI can help improve patients’ outcomes by giving medical personnel access to more accurate histories and reports during diagnoses.
Big Data Means Big Savings
According to a report by McKinsey and Company, if technological innovations in the realm of big data taken into consideration, overall healthcare savings could be enormous.
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A broader data analysis can provide healthcare professionals enough knowledge to determine what risks the population is prone to. These valuable insights can help providers proactively prevent risks before they even affect anyone.
More importantly, big data can pinpoint what is needed where more accurately and precisely, to produce healthier populations, thereby decreasing the overall cost of healthcare in the region.
Opportunities presented by business intelligence here may potentially change the dynamics of the healthcare industry. If you are a healthcare provider and are considering using your data more effectively, you should consult a functionally and technically well-versed BI specialist that can help you create an efficient and effective BI strategy for your business.
So is your medical institution working on implementing a BI strategy yet? What is the most challenging aspect of this implementation so far? Let us know in the comments below, and thanks for reading!
The decisions you make today have a strong bearing on the operations of your business in the future. In order to make sound decisions, your enterprise needs to have the latest operational intelligence information. This is where ‘Business Intelligence’ (BI) comes in and a qualified BI consultant will be able to track your company’s status and operational data very quickly and efficiently.
The global market for BI platforms is witnessing a constant rise that has made it rank among the fastest growing enterprise software industries.
As per Gartner’s Magic Quadrant for BI and Analytics platform from earlier this year, this growth is expected to take place at a CAGR (Compound Annual Growth Rate) of 8.7% through to 2018.
Source: Gartner (February 2015)
Martin Butler, an industry expert, commented on the importance of BI by saying that “an organisation without Business Intelligence bears a striking resemblance to a ship without a rudder” in his latest report.
So in this article, we will look at the reasons your organisation needs a Business Intelligence consultant, along with some tips to find the right BI consultants and hire them at the right time.
Tasks Performed by BI Consultants
An ideal BI consultant for your business will have sound knowledge of the underlying data system, along with programs and tools that create the data. The primary job involves carrying out constant monitoring of the business data and generates timely reports that can be shared with your company’s management on a frequent basis.
One of the most important yet ignored tasks performed by any BI consultant is finding and listing down requirements. The consultant can devise relevant strategies and focus on achieving better results for your business if they have a sound understanding of your business needs.
A smart BI consultant will focus on the business and then determine the technology and tools that need to be used in order to convert a vast pool of data into valuable information.
Having dealt with a number of enterprises, we realise the importance of having a level-headed BI consultant that can offer alternate solutions to emerging challenges with well-defined reports.
This goes a long way in helping the management make informed decisions that turn out to be successful in the long run. Therefore, you can term your BI consultant as the ideal partner for the decision makers of your business.
Rising Need to Hire BI Consultants
As needs are becoming more dynamic, it is getting increasing difficult to make the right, long-term decisions without a strong source of information and intelligence.
You need professionals that can provide updated operational data and information in an efficient manner upon demand. Your management cannot be expected to continuously monitor the data reports and use them to make informed decisions. It requires a specialist to provide the right kind of data and information and this is why you need a qualified BI consultant.
There is no perfect time to hire a BI consultant. You need to get hold of one as early as possible in order to increase your chances of making better decisions that yield quality results for your company. In addition, the sooner you hire a BI consultant, the easier it will be for them to develop an understanding of your requirements.
Skills to Look Out for While Hiring a BI Consultant
- Must be an expert on your company’s underlying data system (IBM DB2 or Microsoft SQL Server or similar)
- Should have sound knowledge of SQL, database functions and data modelling
- Must be able to query recursively at a fast pace (to generate quick reports on demand) and execute analytic functions for complex inter-row calculations
- Must have good knowledge about the source program (Sage ERP or Microsoft Dynamics GP or similar) used to generate data reports
- Must possess excellent communication skills to understand the business requirements clearly, highlight the gaps and showcase valuable information on time
If you are going for a BI project for the first time, you definitely need professional help from agencies that have prior exposure to BI technologies and tools.
Also, you can opt for recruitment agencies that offer experienced BI professionals on contract or for permanent hires. However, you can also opt for BI consultancy firms that provide quality support for all your data-centric BI needs.
There is no room to experiment when it comes to BI for your organisation as a single bad decision can have very far-reaching consequences. Hiring a well-qualified BI consultant or a team of such consultants is a small investment that will pay rich dividends for your business in the long term.
The time and money you spend on hiring the right BI team will eventually help your business grow and increase profitability through well-informed decisions.
So do you plan to hire in-house BI consultants, or outsource? Let us know in the comments below, and thanks for reading!
Any guesses as to what has been named as the ‘sexiest job of the twenty-first century’? According to the Harvard Business Review, it is the job of a data scientist. Surprised? With the amount of data we are generating coupled with the rapid increase in technology, it is no surprise that we need an expert to make sense of all this data and do much more. A data scientist is soon going to become a basic need for most companies. So what exactly does a data scientist do?
Who is a Data Scientist?
The basic job of a data scientist is to analyse data. He does this himself, but he also builds automated systems that do so. Statistics and machines play a big part in the process. A data scientist needs to needs to be good with numbers, but also needs to have a scientific bent of mind.
The job of a data scientist is required today than ever before. Think of the Internet – think how much data is generated there. The number of YouTube videos uploaded each minute, the number of Facebook posts liked every minute, the number of products bought online every minute; and this does not even cover a tenth of the activity that is conducted on the Internet. This kind of data can give invaluable insight and information to a company, depending on what they are looking for. But how do you look for what you want in such a complicated maze and so much sheer volume? This is what a data scientist does.
Data scientists can also make predictions. These predictions are based on science and statistics, and recent trends. For example, Jonathan Goldman revolutionised the click rate on LinkedIn by introducing the ‘People you may know’ feature. At the time, LinkedIn had just begun recently and the problem was that people were not connecting with the people already on the site, the growth was restricted to people inviting their friends and family. Goldman was intrigued by the different connections on the site and the threads between people. Undoubtedly there were people who knew others in their field of work, but how could you connect them? Based on his instincts and some sound statistical study, he refined and implemented the feature. He further added other ways of guessing and predicting connections, and there was a sudden boom in the interactions between visitors and the click throughs.
Such is the effect proper data analysis can have.
Big data and its increasing importance
Big data simply implies data that is ‘big’ – i.e., too huge to process through traditional methods. The modern data scientist makes sense of big data and with the help of recent trends can help a company develop features that can make it extremely useful and customized to a user or a consumer. He can also give insights about almost any stage of the company’s growth, or the impact of a major change or shift in policy or outlook.
Take any application today – almost all of them involve data. Take the example of any shopping portal. It benefits from knowing what you have been looking for, the kind of products you like and which categories you shop most in. Not only does data analysis make it easier for a company to do what encompasses their regular job in a better manner, but it also allows you the possibility to build new and exciting things which were not possible before. Google is a master at this game. Ever typed a wrong spelling into a Google search and noticed Google correct it automatically? It is not just spelling errors that it corrects, Google has a huge database of contexts and phrases which make the search engine top notch and so effective to use.
Look at Amazon. When you look at a product on the website, they show you products that you may like. This feature correlates the product that you are looking at with the browsing or purchase habits of other consumers who looked at other products after this one, and offers you similar recommendations.
Big data is taking over the Internet, and in a big way.
Why do you need a data scientist?
There will be a point in the future when every business will require a data scientist. As every business and every company is getting involved with the Internet in some way or the other, the demand for data scientists will rise exponentially. As of now, however, you may or may not need a data scientist. The question you have to ask yourself is, am I making a product right now that requires a scientist specifically focusing on the data? You need to figure out whether you use conventional data or big data. A key point to remember is that any business that is associated with the consumer directly will benefit greatly from a data scientist.
Most organizations receive chunks of information. Some come from the customer service, some from their IT department and some from their market research team. This information might be random, anything from having a person’s name to his bill details. This random sets of information can start to make sense if organizations can organize this information correctly.
Companies can do well to interlink data that are similar – to be able to strategize better and understand their customers in a better way – a thing we commonly refer to as Big data.
What are we talking about, what is BIG data?
Organizations store data under different heads. Some relate to customer details, some relate to customer mail ID, and some relate to customer needs. Every bit of data is important, but it is useless until it is organized, processed and reduced to simpler forms so to make a sense out of it.
This data can be in the form of text, images, audio, videos – anything imaginable. Most of it would not make sense individually. However, put together in a certain way, they offer useful insights that can offer a detailed knowledge about the observed aspect. Combine your customers birth date with his personal taste and you might just understand how you can help him more with your products or services.
The term BIG DATA refers to large sets of data that are enormously large to be processed by the conventional methods of sorting and thus requires a smarter system to analyze, collect, share, store and simplify.
A system needs to process this data. It is this need that brought up the development of Relational Database Management systems and Hadoop and MongoDB – two big names in the Big Data market.
Talking about Hadoop
Hadoop is a Java-based open source software, developed by the Apache Software foundation in 2011. It is designed to store and process a large amount of data sets on computer clusters.
It consists of HDFS (Hadoop distributed file system), the storage part and the MapReduce, the processing part. The HDFS splits large data blocks into the nodes of the cluster, and this received data is processed parallelly through the package code that manipulates the data so to work efficiently and carry the process.
The data is acquired from the various sources, and these data bits are then passed to a system program that allocates locations to each data bit. These data bits carry this indexed data and move forward in the line to the processing unit. Here the data is harvested and is redirected to the intended nodes that carry the data to different locations to be stored. The program is equipped with the feature to prevent data loose, and machine failure, and so multiple copies of this data is produced and stored at different locations. The data transfer takes place through some reserved protocols and lines so to ensure security and prevent data corruption.
Networks like Facebook, Yahoo, Amazon use these cluster networks for data accumulation.
MongoDB brings solution to the data management hazard
MongoDb also called the NOSQL database, is a cross-platform database management system. Released in 2009 by the MongoDB Inc, this platform has made integration of data much simpler and faster. This is a free and open source software.
This software works as the back-end for many applications. This is among the popular NOSQL database management systems and enterprises like eBay, Craigslist and Viacom use this software for their services.
It utilizes document-oriented approach for data management. This means instead of creating copies of the data and saving it in a different location; this software stores the data in minimum space and least number of documents. Related documents like salary, employee id, and expenses for an enterprise would be compiled into a single document file, and this will not only make data easily available but also easy to manage records. Multiple copies of this compilation are produced to maintain a backup. Also, the read and write operations are initially performed on the primary copies, and the secondary copies remain unaffected until the temporary commands are made permanent. Thus, it can be concluded that the secondary replicas or the document are the read-only type.
Hadoop or MongoDB: A choice too difficult to make?
Both the platforms work on contradicting approaches. Hadoop works on the concept of distributing the data and creating multiple copies while MongoDB defines its algorithm by compiling all the related data into a single document.
Hadoop is designed to function in sync with the presently existing DBMS, while MongoDB is a replacement to these traditional programs.
Hadoop is itself a compilation of several software components while MongoDB is a DBMS in itself.
Can these offer a combined solution?
The MongoDB can help organize and accumulate enormous amounts of data. But this is not it, almost every application of the Big Data Management requires this data to be processed. Now the question that arises is can Hadoop provide this service. It is a good idea to work on but practically achieving this is a difficult task.
Hadoop uses languages like Pig and Hive, which compile as the MapReduce, and using this with the MongoDB is might solve the problem, and this is because the Mongo supports the native MapReduce language.
Working on a data as a whole and bulk processing exerts an excessive load on the hardware but if the load is distributed and the processing is different networks, the transaction takes place more efficiently and quickly.
The CAP (or Bower) theorem states that ‘distributed computing cannot achieve simultaneous Consistency, Availability, and Partition Tolerance while processing data.’ According to this concept, any system can achieve two out of the three above specified goals. This means that it is not possible to solve the problem entirely using a single software.
Some statistics of the platforms using these programs
The following table provides examples of customers using MongoDB together with Hadoop to power big data applications.
Source – mongodb.com
What should you look for?
Surveys and studies state that information generation would become two fold from within the next decade. One system alone would not be capable enough to process such enormous loads. If your organization is large, you need to depend on programs like Hadoop and Mongo together to handle and process data at such a large scale.
The building block of every IT system is ‘data’. Optimal and secured storage of data has always been the key concern for any organisation. The volume of data is growing every minute and it is a difficult task to manage it. This immeasurable digital data is named as big data.
The Internet, by reaching out to each and every computing platform has led to an outburst of information, thereby making big data too big to be handled. A scenario where the data grows beyond the reach of the present management techniques, will lead to total chaos.
Some of the challenges faced with big data are:
- Managing data appropriately is a challenge for any organisation. The optimal use of data in an organised way is a priority task.
- Accessibility and connectivity of huge data are a hurdle. Data points are not integrated and there is no precise platform to manage data between various projects.
- The ever-growing data volume demands a larger technological landscape.
- Security of big data is another important aspect that is of concern to companies.
The following chart explains the growth of big data in the recent years:
Fig 1. Graphical Representation of Rise of Big data
Business Intelligence – A Resourceful Solution for big data:
Business Intelligence (BI) provides a solution to big data in the form of data analysis process. BI includes a variety of tools, applications and methodologies that streamline management of huge data.
This system incorporates technologies that provide a correct decision making platform for the organisations.
Some of the important advantages of Business Intelligence solutions are:
- BI simplifies the process of data management, by collecting data from various sources and organising it, to create reports and for other data analysis requirements.
- BI accelerates the decision making process, thus enabling us to increase the productivity of internal business processes, enhancing efficiency and directing the flow of revenue.
- The simplicity of operation allows BI tools to be accessed by anyone from an IT professional to a business executive.
- BI provides data visualisation in the form of charts and other graphical methods.
- It simplifies advance data analysis functions like data mining, text mining, statistical analysis and predictive analysis.
Microsoft, the Leader in BI Solutions
A Business Intelligence Solution can be leveraged to its fullest, when it is able to collect data from all possible data points.
To be more specific, the solution should be able to provide all users with the required data, anytime. Microsoft has achieved this apical level by utilising the functionalities of various in-house tools.
Exhibiting exceptional competence, Microsoft has emerged as a leading name in the Business Intelligence Solutions segment.
The various formats of Business Intelligence Solutions from Microsoft are more than capable of meeting the growing expectations of the users.
The aspects that make Microsoft the leader are explained below:
Microsoft BI Development
There are various BI technologies provided by the Microsoft BI Development team. The customer can use them independently or utilise a suite of applications. This completely depends on the customer’s requirement.
A good example is Power BI for Office 365. Microsoft allows customers to build a BI system on applications like SQL Server, Sharepoint and Excel.
The complete assembly is called Power BI for Office 365. The result of grouping powerful tools has brought about a revolutionary change in managing Big data.
The various Microsoft BI technologies are:
- MS SQL Server
- Power BI for office
- MS Power View
- MS SSRS (SQL Server Reporting Services)
- MS SQL Server Analysis Services
- MS Excel and Excel Services
- MS SharePoint
- MS Power BI
- MS Power Query
- MS SQL Server Data Tools
- MS SQL Server Integration Services
- MS SQL Server PowerPivot for Excel
- MS PerformancePoint Services
Microsoft BI Stack
Microsoft BI Development has designed various BI tools and applications which the consumer can select from. The requirements of the customer are matched with a specific software tool.
Based on the requirements, Microsoft Business Intelligence can be classified into three categories vis. personal, team and organisational. All these types are interdependent and any change in one aspect can affect the other two.
As an example, an application like Performance Point server uses MS Excel, Office Sharepoint server and Excel services to complement its BI requirement. Every corporate BI system thus has dependable personal and team BI systems.
It is basically related to fetching of data for individual use. Personal BI is independent and is not always connected to databases. The most commonly used personal BI is MS Excel.
Team BI is for a group of individuals working as a team. The system focuses on improving coordination and transfer of data amongst the team members. This helps in acceleration of the decision making process. The most well-known Team BI tool is Office SharePoint Server and Excel Services.
Corporate BI constitutes the Business Intelligence requirements at the organisational level.
It is a collection of tools to support coordination and promote goal congruence. It provides streamlined synchronisation of data and showcases individual efforts in the form of scorecards and strategy maps. The most commonly used Corporate BI is PerformancePoint Server 2007.
The graphical representation of the various levels of Microsoft BI solutions include:
Fig 2. Graphical Representation of Various BI Frameworks
The Business Intelligence Solution has two types of containers vis. the On-premises containers and Cloud containers.
On-premises containers are BI solutions that are implemented in a local setup. Examples of On-premises containers include MS office tools, Share point applications and SQL server.
The Cloud containers deal with the Internet and portable applications. Examples of Cloud containers are Excel Service Power BI and Asure IAAS solutions.
The Microsoft BI Ecosystem
The Microsoft BI Ecosystem is the environment created using various BI tools. The ecosystem supports the ever-changing needs of users
The various important aspects of Microsoft BI Ecosystem include:
This the most widely used feature of the Microsoft BI Ecosystem. The strength lies in its ability to reach every user.
A complete self-service BI solution is delivered through Excel and Office 365. This tool facilitates fast data search, analysis and visualisation. It helps the user in getting valuable insights from the data.
The Power BI for Office 365 allows an even more advanced level of data management. It allows easy sharing, access and integration of data using the cloud technology.
Dashboard and Reports:
Reports are one of the most important features of data analysis. The BI tool, SharePoint server provides a convenient platform for report generation and dashboard management.
Filtering of search attributes, interactive analysis and visualisation are the key features of these Microsoft BI solutions.
Analysis of data from various perspectives is another key feature of any important decision process. The Microsoft BI solution i.e. the SQL Server Analysis Services platform supports multi-dimensional analysis of data.
This feature can be used for visualisation, reporting and data analysis. It supports solution prototyping at the enterprise level.
Microsoft BI Ecosystem allows gathering of data from various sources including historical data. The data analysis is even more precise when a detailed gathering of data i.e. data mining is done.
This helps in projecting a concept more assertively. Some examples of predictive analysis can be the Data Mining Add-ins in Excel or SQL Server Development tools.
Bigger the Data, Sturdy is the Microsoft Solution
The various challenges that IT organisations faced earlier, while dealing with huge data, has found an optimal solution in form of Microsoft Business Intelligence Solutions.
The vast array of BI tools allow the users to customise the solutions as per their requirements. The previously forecasted scenario of unmanageable data at the local and cloud level can be precisely managed by Microsoft BI solution.
Being the pioneers in providing various IT solutions, Microsoft has also stood strong to lead its way towards Business Intelligence Solutions.
So when do you plan to embrace BI? Do you have any points to share? Please feel free to share your opinions below and thanks for reading!