A strategy built using analytics is a set of simple implementable recommendations that efficiently uses the information drawn from the data. An effective and efficient strategy suggests best use of the available business resources. It helps to find solutions for some of the biggest problems faced by the company. The process followed to formulate the strategy might be complex, but the final result is actionable and useful for management.
A typical analytics project or engagement is generally divided into the following four stages:
- Stage 1 - ‘Research’ where the analyst helps to identify and understand the problems and issues that the business is facing or would be encountering in the future. At this step there is significant interaction between the management team and analysts.
- Stage 2 - ‘Plan’ where the analyst helps decide what type of data is required, sources from which the data is to be procured, how the data needs to be prepared for use and what methods to be used for analysis.
- Stage 3 - ‘Execute’ where the analyst explores and analyzes data from various angles. The analysis paves way to interesting results that are shared with the management. Based on these results, strategies are formulated to tackle the problems identified in stage 1.
- Stage 4 - ‘Evaluate’ where the analyst measures the results of the strategies formulated and executed. This stage helps learn and revise future strategies and processes.
Building analytics function requires long term commitment and extensive resources. An organization has an option to seek analytical help from in-house resources or from outside analytical vendors or use both in parallel. Any organization needs to spend considerable time and money to recruit and train in-house analytical help. At times they may not possess the required know-how to recruit such specialized staff or decide on the technologies that would be best suitable for carrying out analysis. In these circumstances they rely on analytical vendors like IQR Consulting. Such vendors can closely work with the management team to help the organization to adopt analytics. The organization has to trust and co-operate with the vendors while sharing their data and researching it to make the analytics engagement a success. Organizations can follow another model in which they build an internal team to manage their relationships with an external analytical vendor. Many analytically mature companies resort to this to supplement their internal efforts.
Businesses are using analytics to make more informed decisions and to plan ahead. It helps businesses to uncover opportunities which are visible only through an analytical lens. Analytics helps companies to decipher trends, patterns and relationships within data to explain, predict and react to a market phenomenon. It helps answer the following questions:
- What is happening and what will happen?
- Why is it happening?
- What is the best strategy to address it?
Data Analytics is about understanding your data and using that knowledge to drive actions. It reveals the trends and outliers within the data which might be otherwise difficult to note. It is a scientific way to convert raw data into information that helps guide difficult decisions. A number of statistical tools and softwares are available to perform data analytics. The nature of data and the problem which needs to be solved using the insights from data guides the choice of statistical tools and techniques. Domain knowledge and expertise are also very important to interpret and apply the results obtained from analytics. Lastly, in our experience, the best data analysts are those who have the ability to dig into the data but can also layer common sense and domain knowledge into their recommendations.
Eventually the big data hype will wear off, but studies show that big data adoption will continue to grow. With a projected $16.9B market by 2015 (Wikibon goes even further to say $50B by 2017), it is clear that big data is here to stay. However, the big data talent pool is lagging behind and will need to catch up to the pace of the market. McKinsey & Company estimated in May 2011 that by 2018, the US alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. The emergence of big data analytics has permanently altered many businesses’ way of looking at data. Big data can take companies down a long road of staff, technology, and data storage augmentation, but the payoff – rapid insight into never-before-examined data – can be huge. As more use cases come to light over the coming years and technologies mature, big data will undoubtedly reach critical mass and will no longer be labeled a trend. Soon it will simply be another mechanism in the BI ecosystem
Another reason big data is starting to go mainstream is the fact the tools to analyze it are becoming more accessible. For decades, arcplan partners Teradata (NYSE: TDC), IBM (NYSE: IBM), and Oracle (NasdaqGS: ORCL) have provided thousands of companies with terabyte scale data warehouses, but there is a new trend of big data being stored across multiple servers that can handle unstructured data and scale easily. This is due to the increasing use of open source technologies like Hadoop, a framework for distributing data processing across multiple nodes, which allows for fast data loading and real-time analytic capabilities. In effect, Hadoop allows the analysis to occur where the data resides, but it does require specific skills and is not an easy technology to adopt. Analytic platforms like arcplan, which connects to Teradata and SAP HANA, SAP’s (NYSE: SAP) big data appliance, allow data analysis and visualization on big data sets. So in order to make use of big data, companies may need to implement new technologies, but some traditional BI solutions can make the move with you. Big data is simply a new data challenge that requires leveraging existing systems in a different way.
Big data is often boiled down to a few varieties including social data, machine data, and transactional data. Social media data is providing remarkable insights to companies on consumer behavior and sentiment that can be integrated with CRM data for analysis, with 230 million tweets posted on Twitter per day, 2.7 billion Likes and comments added to Facebook every day, and 60 hours of video uploaded to YouTube every minute (this is what we mean by velocity of data). Machine data consists of information generated from industrial equipment, real-time data from sensors that track parts and monitor machinery (often also called the Internet of Things), and even web logs that track user behavior online. At arcplan client CERN, the largest particle physics research center in the world, the Large Hadron Collider (LHC) generates 40 terabytes of data every second during experiments. Regarding transactional data, large retailers and even B2B companies can generate multitudes of data on a regular basis considering that their transactions consist of one or many items, product IDs, prices, payment information, manufacturer and distributor data, and much more. Major retailers like Amazon.com, which posted $10B in sales in Q3 2011, and restaurants like US pizza chain Domino’s, which serves over 1 million customers per day, are generating petabytes of transactional big data. The thing to note is that big data can resemble traditional structured data or unstructured, high frequency information.
"Big data" is an all-inclusive term used to describe vast amounts of information. In contrast to traditional structured data which is typically stored in a relational database, big data varies in terms of volume, velocity, and variety. Big data is characteristically generated in large volumes – on the order of terabytes or exabytes of data (starts with 1 and has 18 zeros after it, or 1 million terabytes) per individual data set. Big data is also generated with high velocity – it is collected at frequent intervals – which makes it difficult to analyze (though analyzing it rapidly makes it more valuable). Additionally, big data is usually not nicely packaged in a spreadsheet or even a multidimensional database and often includes unstructured, qualitative information as well.
The cost of Business Intelligence system implementation depends on many factors. First of all it depends on a range of the whole process (content-related area, number of users, number of data sources and level of their integration ect.) and tool selection (producer/way of license). To optimize costs, apart from implementation company, own human-resources may be considered. IT and business stuff can prepare reports.
Business Intelligence system implementation doesn’t require intervention of any used systems. An additional configuration of systems in sometimes necessary to connect them with BI tools
Business Intelligence Systems are great fulfillment of existing database and reporting systems. Reports in ERP systems are often insufficient and don’t allow for advanced and cross-sectional analysis, especially when report should include data from different sources. Integration of exisisting tools with BI system open an acces to all data, it allows to epitomize of the company situation. The unified data is a great source of reports and dashboards. They can be easily distributed to many consumers. A cooperation with professional business partner is indispensable. It will help to identify all necessary attributes and key measures for indicated analysis and represent them clearly It will facilitate decision-making process by giving the right to inspect the key operational information. It also allows to define critical problems.
A Consultant works in partnership with clients on specific projects either as part of an outsourced team or to supplement an existing one. Consultants are relocated to client centres for a contracted amount of time to plan and develop business or technology systems and may also be asked to provide expert advice related to their specialism. Consultancy is a flexible career as projects can range from a few months to years depending on its size. This can lead to opportunities for travel and a variety of challenging and exciting assignments with a range of clients. FDM Consultants are employed by FDM to provide professional business and IT consultancy to our clients.
IDB Solutions Consulting is a dynamic consultancy specialising in helping companies to better manage their customer relationships – and make better business decisions – through the use of leading -edge software applications from Oracle. Our work is project-based, which means that no two days will be the same. You’ll be constantly learning, and you’ll have everyone in the organisation behind you helping you to be the best. You’ll be given a lot of responsibility from the start and, if you excel, opportunities for further responsibility will come. Working in a growing company, you will have the opportunity to shape your career in any way you wish. We are an organisation invested in developing your knowledge and skills. Our main goal is to support you in becoming an expert in your field.
This decision is a very complex task. The implementation of a new system will have significant impact on the functioning and organization of a company. The choice of a provider should be preceeded with the company’s needs analysis. It is very important to specify funcional requirements of the system and determine its meaning in organization. Then it is possible to create an offer inquiry and mechanism of offers evaluation. The solution can may include tools from differents software producers. For example, the data warehouse may be created with the use of Microsoft tools and the dashboards – Qlik View. when choosing appropriate tools, the best idea is to consult with the experienced Business Intelligence specialists. They will help to choose optimal and well integrated set of components.
In most cases Business Intelligence systems have both web and desktop tools. The tools targeted at analysts and used for making reports and analyze data, almost always have web interface. The tools targeted at people working on integrating or modeling data mostly have desktop interface.
IT market is very dinamic and new Business Intelligence systems constantly appear. It is very hard to indicate which system is the best one. See more: How to choose Business Intelligence software provider?