3 items tagged "advanced analytics"

  • A Shortcut Guide to Machine Learning and AI in The Enterprise


    Predictive analytics / machine learning / artificial intelligence is a hot topic – what’s it about?

    Using algorithms to help make better decisions has been the “next big thing in analytics” for over 25 years. It has been used in key areas such as fraud the entire time. But it’s now become a full-throated mainstream business meme that features in every enterprise software keynote — although the industry is battling with what to call it.

    It appears that terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers. The term Cognitive Computing seemed to be poised to win, but IBM’s strong association with the term may have backfired — journalists and analysts want to use language that is independent of any particular company. Currently, the growing consensus seems to be to use Machine Learning when talking about the technology and Artificial Intelligence when talking about the business uses.

    Whatever we call it, it’s generally proposed in two different forms: either as an extension to existing platforms for data analysts; or as new embedded functionality in diverse business applications such as sales lead scoring, marketing optimization, sorting HR resumes, or financial invoice matching.

    Why is it taking off now, and what’s changing?

    Artificial intelligence is now taking off because there’s a lot more data available and affordable, powerful systems to crunch through it all. It’s also much easier to get access to powerful algorithm-based software in the form of open-source products or embedded as a service in enterprise platforms.

    Organizations today have also more comfortable with manipulating business data, with a new generation of business analysts aspiring to become “citizen data scientists.” Enterprises can take their traditional analytics to the next level using these new tools.

    However, we’re now at the “Peak of Inflated Expectations” for these technologies according to Gartner’s Hype Cycle — we will soon see articles pushing back on the more exaggerated claims. Over the next few years, we will find out the limitations of these technologies even as they start bringing real-world benefits.

    What are the longer-term implications?

    First, easier-to-use predictive analytics engines are blurring the gap between “everyday analytics” and the data science team. A “factory” approach to creating, deploying, and maintaining predictive models means data scientists can have greater impact. And sophisticated business users can now access some the power of these algorithms without having to become data scientists themselves.

    Second, every business application will include some predictive functionality, automating any areas where there are “repeatable decisions.” It is hard to think of a business process that could not be improved in this way, with big implications in terms of both efficiency and white-collar employment.

    Third, applications will use these algorithms on themselves to create “self-improving” platforms that get easier to use and more powerful over time (akin to how each new semi-autonomous-driving Tesla car can learn something new and pass it onto the rest of the fleet).

    Fourth, over time, business processes, applications, and workflows may have to be rethought. If algorithms are available as a core part of business platforms, we can provide people with new paths through typical business questions such as “What’s happening now? What do I need to know? What do you recommend? What should I always do? What can I expect to happen? What can I avoid? What do I need to do right now?”

    Fifth, implementing all the above will involve deep and worrying moral questions in terms of data privacy and allowing algorithms to make decisions that affect people and society. There will undoubtedly be many scandals and missteps before the right rules and practices are in place.

    What first steps should companies be taking in this area?
    As usual, the barriers to business benefit are more likely to be cultural than technical.

    Above all, organizations need to make sure they have the right technical expertise to be able to navigate the confusion of new vendors offers, the right business knowledge to know where best to apply them, and the awareness that their technology choices may have unforeseen moral implications.

    Source: timoelliot.com, October 24, 2016


  • Distinguishing between advanced analytics and business intelligence

    Distinguishing between advanced analytics and business intelligence

    Advanced analytics and business intelligence (BI) have more or less the same objective: use data to drive insights that inform business strategy. So what’s the difference? 

    What is business intelligence? 

    Business intelligence is an umbrella term for software and services that provide comprehensive yet straightforward insights about an organization’s current state. Think routine reporting or dashboarding, where data is clearly legible for stakeholders to understand month by month. Examples of business intelligence use cases abound, some of which include unifying data to better track marketing leads or to manage shipping operations across a fleet of trucks. Business intelligence is by no means easy, but it is grounded in practical, everyday uses of data. 

    What is advanced analytics? 

    Advanced analytics employs the use of sophisticated tools and techniques that surpass traditional business intelligence capabilities. Like business intelligence, it is a wide-reaching term that involves many methods and lends itself to many possible use cases.

    Advanced analytics is not meant to replace business intelligence but to augment its efforts. It strives to ask deeper questions of the data, generating insights that not only indicate how the business is currently performing but where its future is headed. If we consider that business intelligence largely aims to point out strengths and weaknesses in current business processes, advanced analytics has the potential to make recommendations and predictions as to how to steer the organization forward. 

    Examples of 5 advanced analytics techniques 

    Let’s take a closer look at some of the techniques that fall under the category of advanced analytics. Rarely will organizations need to use all of these techniques at once as a part of their advanced analytics integration; rather, they are merely some of the many tools in the toolkit of a data professional. 

    1. Forecasting

    Forecasting is the technique of analyzing historical data to predict future outcomes. It considers prior trends to recommend how organizations should plan ahead, such as stocking more inventory for a historically popular sales day. Forecasts can be extremely accurate, but their reliability depends upon the relevance and availability of historical data, as well as the time period to be forecasted.

    2. Machine learning

    Machine learning is the process of training a computer to predict outcomes without it being specifically programmed to do so. Machine learning models are built to model the desired behavior, and as the model is fed more and more training data, its accuracy in predicting outcomes increases. Data, and lots of it, is the key to effective machine learning models.

    3. Data mining and pattern matching

    Data mining is the process of uncovering patterns in large batches of raw data for further analysis. Analysts often don’t know what’s in data warehouses or what they should be looking for; data mining techniques, such as pattern matching, help source the right data from data warehouses based upon connections in the data.

    4. Semantic analysis

    Semantic analysis is the act of determining meaning from text data. By way of semantic analysis, computers can “read” full documents by analyzing its grammatical structure and the relationship of individual words. The technique is particularly useful for marketing teams to be able to analyze social media data or for customer service teams to better understand the effectiveness of online customer support.

    5. Complex event processing

    Complex event processing is the act of aggregating huge volumes of data to help determine the cause-and-effect relationships for any given event. By matching incoming events against a pattern, complex event processing can shed light as to what is happening.

    Benefits of advanced analytics

    It’s widely recognized that an advanced analytics integration offers a competitive edge. Just a few of the benefits that advanced analytics can deliver include: 

    • Better decision-making
      Advanced analytics delivers valuable insights that allow organizations to make better decisions, adjust their company strategy, and plan for the future. 
    • Saved costs
      Identifying overspend or leaking costs through advanced analytics can have a huge impact on the budget over time.
    • Increased innovation
      Through advanced analytics, organizations have developed innovative new products, processes, or sales/marketing strategies that have given them a leg up from the competition.

    Challenges of advanced analytics

    Many organizations encounter roadblocks along their advanced analytics journey, which prevent them from fully realizing these benefits. According to a 2018 McKinsey survey, “fewer than 20 percent [of companies] have maximized the potential and achieved advanced analytics at scale.” Some of the top challenges of advanced analytics include:

    • Cost
      Advanced data analytics will prove its ROI over time, but the upfront costs can be rather costly. Investing in infrastructure and talent, as well as the time required for data strategy and deployment, can be intimidating for organizations to take on.
    • Working with data from multiple sources
      Effective analytics should employ as many data sources as necessary, but gathering and integrating all of these data sources can be challenging.
    • Inaccessible data
      Even after the appropriate amount of data is gathered and centralized, if that data isn’t made accessible to the analysts that need to use it, it will serve little value to the organization.
    • Skills shortage
      Data scientists and data engineers are costly resources and difficult to source. Though user-friendly technologies have lowered the barrier to advanced analytics, many organizations still want a foundational data science team.
    • Poor quality data
      Harvard Business Review called poor quality data “enemy number one” to machine learning initiatives, and that extends to all facets of advanced analytics. If data hasn’t been vetted to meet data quality standards or properly prepared for the requirements of the analysis at hand, it will only lead to faulty or misleading insights. 

    Data preparation & advanced analytics

    Data preparation accounts for up to 80% of total analytic time. It’s where analysts can encounter a minefield of analytic challenges. But, it also presents the biggest opportunity for improvement. Succeed at data preparation and odds are, you’ll see far less advanced data analytics challenges. 

    Traditional data preparation methods like extract, transform, and load (ETL) tools or hand-coding are time-consuming and bar analysts from the process of transforming their own data. Recently, organizations have invested in modern data preparation platforms as a part of their advanced analytics integration, which allows organizations to:

    • Easily connect to a diverse range of data sources. 
    • Identify data quality issues through a visual interface. 
    • Involve non-technical analysts in the process of preparing data. 
    • Integrate structured and unstructured data of any size. 
    • Reduce the total time spent preparing data by up to 90%. 

    Author: Matt Derda

    Source: Trifacta

  • Lessons From The U.S. Election On Big Data And Algorithms

    The failure to accurately predict the outcome of the elections has caused some backlash against big data and algorithms. This is misguided. The real issue is failure to build unbiased models that will identify trends that do not fit neatly into our present understanding. This is one of the most urgent challenges for big data, advanced analytics and algorithms.  When speaking with retailers on this subject I focus on two important considerations.  The first is that convergence of what we believe to be true and what is actually true is getting smaller.


    This is because people, consumers, have more personal control than ever before.  They source opinions from the web, social media, groups and associations that in the past where not available to them.  For retailers this is critical because the historical view that the merchandising or marketing group holds about consumers is likely growing increasingly out of date.  Yet well meaning business people performing these tasks continue to disregard indicators and repeat the same actions.  Before consumers had so many options this was not a huge problem since change happened more slowly.  Today if you fail to catch a trend there are tens or hundreds of other companies out there ready to capitalize on the opportunity.  While it is difficult to accept, business people must learn a new skill, leveraging analytics to improve their instincts.

    The second is closely related to the first but with an important distinction; go where the data leads. I describe this as the KISS that connects big data to decisions.
    The KISS is about extracting knowledge, testing innovations, developing strategies, and doing all this at high speed. The KISS is what allows the organization to safely travel down the path of discovery – going where the data leads – without falling down a rabbit hole.
    Getting back to the election prognosticators, there were a few that did identify the trend.  They were repeatedly laughed at and disregarded. This is the foundation of the problem, organizations must foster environments where new ideas are embraced and safely explored.  This is how we will grow the convergence of things we know. 
    Source: Gartner, November 10, 2016

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