{"id":6744,"date":"2012-03-07T12:48:14","date_gmt":"2012-03-07T12:48:14","guid":{"rendered":"http:\/\/www.smartdatacollective.com\/index.php\/post\/3-ways-big-data-analytics-will-change-enterprise-performance-management\/"},"modified":"2012-03-07T12:48:14","modified_gmt":"2012-03-07T12:48:14","slug":"3-ways-big-data-analytics-will-change-enterprise-performance-management","status":"publish","type":"post","link":"https:\/\/www.smartdatacollective.com\/3-ways-big-data-analytics-will-change-enterprise-performance-management\/","title":{"rendered":"3 Ways \u2018Big Data Analytics\u2019 Will Change Enterprise Performance Management"},"content":{"rendered":"

Success of companies \u2013 big or small \u2013 will increasingly depend on their ability to capture, analyze and gain insights from data.<\/p>\n

 While Enterprise Performance Management (EPM) has always been about collecting, analyzing and reporting of data to support management decision making, the emergence \u2018Big Data Analytics\u2019 is going to change EPM as we know it. \u2018Big Data Analytics\u2019 is one of the biggest business buzz words of 2012, so let me outline some of my thoughts on how this will change EPM in most companies.<\/p>\n

<\/p>\n

Success of companies \u2013 big or small \u2013 will increasingly depend on their ability to capture, analyze and gain insights from data.<\/p>\n

 While Enterprise Performance Management (EPM) has always been about collecting, analyzing and reporting of data to support management decision making, the emergence \u2018Big Data Analytics\u2019 is going to change EPM as we know it. \u2018Big Data Analytics\u2019 is one of the biggest business buzz words of 2012, so let me outline some of my thoughts on how this will change EPM in most companies.<\/p>\n

In their EPM efforts, most companies are collecting Key Performance Indicators (KPIs) and business metrics, analyze them and report the results of their analysis to inform management decision making. Having said that \u2013 most companies also struggle to identify the key strategic indicators and often end up reporting a random set of metrics that don\u2019t lead to any real insights and don\u2019t support decision making \u2013 but this is by-the-by.<\/p>\n

Other companies have taken it a step further with data warehouses that not only hold their KPIs but also large quantities of transactional and operational data, which they can use to extract even more insight from. In the past it was only those companies with the biggest budgets and best skills who were able to benefit from sophisticated business analytics.<\/p>\n

In my book \u2018The Intelligent Company<\/a>\u2019 I talk about companies like Tesco, Google and eBay and how they are able to gain competitive advantages from this approach. What\u2019s more, many of those companies are also able to monetize their data analytics by e.g. selling the insights they gain to partners, suppliers or customers.<\/p>\n

In this traditional world data is usually collected and stored in large data warehouses that work a bit like physical warehouses: Data is structured and stored in rows and columns and need a logical inventory system to allow us to find and use the data. However, this world is changing very quickly.<\/p>\n

The rapid evolution of computer technology combined with the stellar growth in social media are just two factors that have given rise to Big Data Analytics. \u2018Big Data\u2019 is not really clearly defined yet but it generally refers to the more messy types of data that we can\u2019t easily put into rows and columns and that are too big to store and analyze in our traditional data warehouse systems.<\/p>\n

Examples of Big Data include the vast and ever changing amounts of data generated in social networks where we have (unstructured) conversations with each other, video data, Internet search and browser logs, as well as the ever-growing amount of data generated by the sensors and chips in our smart phones and tablets.<\/p>\n

The idea behind Big Data Analytics is basically that companies use and analyse lager and more complex sets of data to inform their decision-making \u2013 data sets that were previously unthinkable to analyse. Analysing Big Data would, for example, allow companies to better understand customer or employee behaviours by using their own transactional data and web logs and combining this with video tracking, social media postings and search engine data to get much richer insights.<\/p>\n

So what are some of the key implications of \u2018Big Data Analytics\u2019 for Enterprise Performance Management as we know it today:<\/p>\n

    \n
  1. More data to play with<\/strong> \u2013 while traditional KPIs will remain vital business navigation tools, they will be increasingly supplemented with the analysis of larger, unstructured data sets. It is important that companies spend some time now to think about the traditional KPIs they need to inform decision making and how each of these could be enriched with the analysis of bigger data sets. An example might be using the Net Promoter Score to assess customer satisfaction and potential loyalty levels to a specific brand or product and supplementing this with the analysis of Facebook and Twitter mentioned of this brand or product. My latest book \u2018Key Performance Indicators \u2013 the 75+ measures every manager needs to know<\/a>\u2019 might be a good starting point for identifying the KPIs for your business.<\/li>\n
  2. Analytics for everyone<\/strong> \u2013 while in the past companies needed a large IT and analytics budget to collect, store and analyze larger volumes of data, today there are a lot of free tools out there which companies can use to start analyzing big data. Here are some examples of tools companies would be mad not to use: Any company can use \u2018Google Trends\u2019 to do market research and analyze vast amounts of trend data,  \u2018Social Mention\u2019 is a tool that allows you to track social media mentions of your company and of your brands \u2013 it even provides an analysis of the sentiment of the comments (e.g. whether they were positive or negative), initially designed as a paid-for-service \u2018Google Analytics\u2019 is a free tools that allows you to analyze web traffic on your website. Then there are more industry specific tools out there like Trip Advisor which captures feedback from travellers about hotels, restaurants and attractions. Trip Advisor now provides dashboards for hotels which allows them to analyze and visualise customer and feedback trends.<\/li>\n
  3. Technology change<\/strong> \u2013 while companies have traditionally seen their EPM technology as data warehouses with analytics and reporting software on top, it is important now to look out for newer technology such as cloud computing -where data is stored and accessed over a network or the Internet, Software as a Service (SaaS) \u2013 where analytics software applications can be accessed over the internet and \u2018rented\u2019 for the time it is needed, Hadoop – an open source software framework that supports large volume data processing by distributing raw data across different systems, as well as in-memory data analytics -where data is no-longer stored and queried in disks but instead is processed much faster at runtime.<\/li>\n<\/ol>\n

    So in essence, Big Data Analytics will change the way companies will do EPM and it is important to look at the three points I outline and think about the implications of these for your own company (big or small).  <\/p>\n

    More soon,<\/p>\n

    Bernard<\/p>\n","protected":false},"excerpt":{"rendered":"

    Success of companies \u2013 big or small \u2013 will increasingly depend on their ability to capture, analyze and gain insights from data.  While Enterprise Performance Management (EPM) has always been about collecting, analyzing and reporting of data to support management decision making, the emergence \u2018Big Data Analytics\u2019 is going to change EPM as we know […]<\/p>\n","protected":false},"author":211,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"categories":[15,2,24,31,7,17,22,25,16],"tags":[116,252,360,245],"class_list":{"0":"post-6744","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-analytics","7":"category-business-intelligence","8":"category-cloud-computing","9":"category-commentary","10":"category-data-warehousing","11":"category-decision-management","12":"category-hadoop","13":"category-social-data","14":"category-unstructured-data","15":"tag-analytics","16":"tag-big-data","17":"tag-enterprise-performance-management","18":"tag-information-management"},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/posts\/6744"}],"collection":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/users\/211"}],"replies":[{"embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/comments?post=6744"}],"version-history":[{"count":0,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/posts\/6744\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/media?parent=6744"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/categories?post=6744"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/tags?post=6744"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}