Enhance your sales process analysis for Quality Assurance

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Hi my name is Will and i'm going to give   you a step-by-step guide to the  data analysis process, let's go! in this video we're going to go through the five  key stages of the data analysis process we're   going to give you an overview and an introduction  to each of these stages as well as looking at some   of the tools you'll use to undertake these stages  so let's dive into step one defining the question   the first step in your data analysis process  or any data analysis process is to define your   objective in data analytics terms this is called  the problem statement defining your objective   means coming up with a hypothesis and figuring  out how exactly to test it you can start by asking   what business problem am i trying to solve now i  know this might sound straightforward but it can   actually be trickier than it seems for instance  your organization's senior management might pose a   question such as why are we losing customers it's  possible though that this doesn't get to the core   of the problem a data analyst job is to understand  the business and the business's goals as a data   analyst you need to understand this in enough  depth that they can frame the problem the right   way to give you a practical example let's say you  work for a fictional company for example we'll   call it top notch learning this fictional company  top notch creates custom training software for its   clients in this example top notch is excellent  at securing new clients but unfortunately top   notch has much lower repeat business as such as a  data analyst your question might not be why are we   losing customers but which factors are negatively  impacting the customer experience or even better   yet how can we boost customer retention whilst  minimizing costs now you've identified the problem   you need to find which data is going to help  you solve this issue this is where your business   acronym comes in again for instance perhaps  you've noticed that the sales pipeline for new   customers is very slick but the production team  is extremely inefficient knowing this you could   hypothesize the sales process actually wins a lot  of new clients but the customer experience well   it's kind of lacking could this be the reason that  customers aren't coming back what sources of data   will help you answer this question as a data  analyst considering all these things will help   you define the question and help you solve the  problem at hand there's also a number of tools   that can help you define your objective defining  your objective is mostly about soft skills so   business knowledge and lateral thinking but you'll  also need to consider business metrics and key   performance indicators and these are called KPIs  monthly reports can help you track problem points   in the business there are lots of tools out there  on the market that can analyze this business data   tools like Databox and DashaRoo there's also free  open source software like Grafana freeboard and   Dashbuilder these are fantastic for producing  simple dashboards both at the beginning and at   the end of the data analysis process so that was  step one defining the objective onto step two step   two collecting the data once you've established  your objective you'll need to create a strategy   for collecting and aggregating the appropriate  data a key part of this is determining which   data you need this might be quantitative data or  numeric data eg sales figures or monthly reports   or qualitative descriptive data such as customer  reviews all of this data fits into one of three   categories first party second party and third  party data let's explore each one briefly now   what is first party data first party data is data  that you or your company has directly collected   from customers it might for example come in the  form of transactional tracking data or information   from your customer relationship management  system your CRM system whatever it source   first party data is usually collected in a clear  and structured way other sources of first party   data might include customer satisfaction surveys  focus groups interviews or direct observation   let's talk about second party data to enrich your  analysis you might want to secure a secondary data   source second party data is simply the first  party data of other organizations this might   be available directly from the company or from  private marketplace the main benefit of second   party data is that it's usually structured  and although it's less relevant than first   party data it tends to be reliable examples of  second party data include website app or social   media activity like online purchase history or  shipping data so lastly what is third-party data   third-party data is data that has been collected  and aggregated from numerous sources from a third   party often but not always third-party data  contains a lot of unstructured data or big data   many organizations collect this big data to create  industry reports or to conduct market research the   research and advisory firm Gartner is a good real  world example of an organization that collects big   data and then sells it on to other companies open  data repositories and government portals are also   sources of third third-party data let's take  a moment to look at some of the tools that you   can use to collect data once you've devised this  data strategy ie you've identified what data you   need and how best to go about collecting it there  are many tools that you can use to help you one   thing you'll need regardless of industry or area  of expertise is a data management platform or DMP   a DMP is a piece of software which allows you to  identify and aggregate data from numerous sources   before they're manipulating them segmenting them  and so on there are many DMPs available some   well-known enterprise DMPs include salesforce DMP,  SAAS and the data integration platform Xplenty if   you want to play around you can also try some open  source platforms like Pymcore or Dswarm on to step   three cleaning the data once you've collected your  data the next step is to get it ready for analysis   this means cleaning or scrubbing it and this is  crucial to make sure that you're working with   high quality data key data cleaning tasks include  removing major errors duplicates or outliers all   of which are problems when you aggregate data  from numerous sources removing unwanted data   points so extracting irrelevant observations that  have no bearing on your intended analysis bringing   structure to your data or general housekeeping  so for example fixing typos or layout issues   which will help you map or manipulate your data  more easily and finally it helps filling in major   gaps as you're tidying up you might notice that  important data is missing once you've identified   these gaps you can go about filling them a good  data analyst will spend about 70 to 90 of their   time cleaning data this might sound excessive but  focusing on the wrong data or analyzing error in   this data will severely impact your results it  might even send you back to square one so whatever   you do don't rush this step let's have a look  at some of the tools that you can use to clean   your data cleaning data manually especially large  data sets can be incredibly daunting but luckily   there are many tools available to streamline this  process open source tools such as open refine are   excellent for basic data cleaning as well as high  level exploration however free tools offer limited   functionality for very large data sets now i know  this sounds like a data zoo but python libraries   such as pandas and some r packages are better  suited to heavy data scrubbing you will of course   need to be savvy with languages alternatively  enterprise tools are also available for example   data ladder which is one of the highest rated data  matching tools in the industry there are many more   why don't you see which data cleaning tools you  can find online share your free tools in the   comments below so that was step three cleaning  the data on to step four analyzing that data   finally once you've cleaned your data now comes  the fun bit analyzing it the type of data analysis   you conduct largely depends on what your goal is  but there are many techniques available univariate   or bivariate analysis time series analysis and  regression analysis are just a few you might   have heard of more important than the different  types though is how you apply them this depends   on what types of insights you're hoping to gain  broadly speaking all types of data analysis fit   into the four following categories descriptive  analysis which is analysis which identifies what   has already happened this is a common first step  that companies do before proceeding with deeper   explorations diagnostic analysis where the focus  is on understanding why something has happened   it is literally the diagnosis of a problem just  as a doctor uses the symptoms to diagnose the   patient's disease predictive analysis which is  where you identify future trends by the analysis   of historical data predictive analysis is commonly  used by businesses to forecast future growth and   lastly prescriptive analysis which allows  you to make recommendations for the future   this is the final step in the analytics part  of the process but it's also the most complex   this is because it incorporates aspects of all  the other analyses that we've described today   step 5 sharing your results you've finished  carrying out your analyses you have your insights   the final step of a data analysis process is to  share these insights with the wider world or at   least with your organization's stakeholders this  is actually more complex than just sharing the raw   results of your work it involves interpreting the  outcomes and presenting them in a manner which is   digestible to everybody that's in the room since  you'll also present your work to decision makers   it's very important that the insights that you  share are 100 clear and also unambiguous for   this reason data analysts usually use reports  dashboards and interactive visualizations to   support their findings how you interpret and  present results will often influence the direction   of the business depending on what you share  your organization might decide to restructure to   launch a new product or close an entire division  that's why it's very important to present all the   evidence that you gathered and not to cherry-pick  data ensuring that you cover everything in a clear   and concise way will prove that your conclusions  are scientifically sound and based on facts on the   flip side it's important to highlight any gaps  in the data or to flag any insights that might   be open to interpretation remember that honest  communication is an important part of the process   it will help the business but it will also help  you to excel at your job there's a ton of tools   for interpreting and sharing your findings these  tools are suited to different experience levels   but popular tools that require no coding skills  include Google Charts, Tableau, Datawrapper and   Infogram if you're familiar with python and  r there are also many data visualization   libraries and packages available for instance  check out the Python libraries Plotty, Seaborn   and Matplotlib whichever data visualization  tools you use make sure that you polish up your   presentation skills too visualization is great  but communication is key so hopefully now you   have a better idea of the data analysis process  CareerFoundry have an awesome data analytics short   course and you can sign up for free by the link in  the description thanks for joining us today i hope   this video has been helpful here's another video  i made about data analytics which is just for you