Data analysis steps
Data analysis process: 5 steps to better decision most businesses and government agencies, lack of data isn’t a problem. In fact, it’s the opposite: there’s often too much information available to make a clear so much data to sort through, you need something more from your data:You need to know it is the right data for answering your question;. Need to draw accurate conclusions from that data; need data that informs your decision making short, you need better data analysis. With the right data analysis process and tools, what was once an overwhelming volume of disparate information becomes a simple, clear decision improve your data analysis skills and simplify your decisions, execute these five steps in your data analysis process:Step 1: define your your organizational or business data analysis, you must begin with the right question(s). 2: set clear measurement step breaks down into two sub-steps: a) decide what to measure, and b) decide how to measure it. Decide what to the government contractor example, consider what kind of data you’d need to answer your key question. Decide how to measure ng about how you measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredits your analysis later on. Your question clearly defined and your measurement priorities set, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind:Before you collect new data, determine what information could be collected from existing databases or sources on hand. Collect this data ine a file storing and naming system ahead of time to help all tasked team members collaborate. This process saves time and prevents team members from collecting the same information you need to gather data via observation or interviews, then develop an interview template ahead of time to ensure consistency and save your collected data organized in a log with collection dates and add any source notes as you go (including any data normalization performed). This practice validates your conclusions down the you’ve collected the right data to answer your question from step 1, it’s time for deeper data analysis. Begin by manipulating your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in excel. A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum and standard deviation of your data – just be sure to avoid these five pitfalls of statistical data you manipulate data, you may find you have the exact data you need, but more likely, you might need to revise your original question or collect more data. Either way, this initial analysis of trends, correlations, variations and outliers helps you focus your data analysis on better answering your question and any objections others might this step, data analysis tools and software are extremely helpful. If you need a review or a primer on all the functions excel accomplishes for your data analysis, we recommend this harvard business review 5: interpret analyzing your data and possibly conducting further research, it’s finally time to interpret your results.
As you interpret your analysis, keep in mind that you cannot ever prove a hypothesis true: rather, you can only fail to reject the hypothesis. Meaning that no matter how much data you collect, chance could always interfere with your you interpret the results of your data, ask yourself these key questions:Does the data answer your original question? Your interpretation of the data holds up under all of these questions and considerations, then you likely have come to a productive conclusion. The only remaining step is to use the results of your data analysis process to decide your best course of following these five steps in your data analysis process, you make better decisions for your business or government agency because your choices are backed by data that has been robustly collected and analyzed. With practice, your data analysis gets faster and more accurate – meaning you make better, more informed decisions to run your organization most to draw the most accurate conclusions from your data? Click below to download a free guide from big sky associates and discover how the right data analysis drives success for your y policysite mapdesign by hinge© big sky in the data analysis to measure and ement generally refers to the assigning of numbers to indicate different values of your research involved determining if there was a relationship between height and weight of dogs, it would make sense to measure the height and weight of dogs using a scale. And validity of -existing test of basic e state achievement rd achievement er individual achievement range achievement ck johnson psychoeducational -social/health red society of physiotherapy database of es of personality and social psychological ication research you have decided what types of data you need for your study, you can determine whether your data can be gathered from existing sources/databases or whether you need to collect new information through other means. Even when using existing data, it is important to know how the data was collected so that the limitations of the generalizability of results may be determined and the proper analyses may be researcher should be able to select and defend an appropriate method of data ting data/sampling monkey design size formula and ng (probability vs. And displaying es of central tendency indicate what is typical of the average es of variance indicate the distribution of the data around the rd deviation and ation refers to the degree to which two variable move in sync with one ation r plots and cal displays are powerful tools for teaching and persuasion. A picture is really worth a thousand words as many people understand pictures better than a ng all that data - tools for displaying tical software with info for esis testing using ers guide to sas and stata state spss ling and using ing data and interpreting esis testing is the use of statistics to determine the probability that a given hypothesis is the beginning of your research project, you no doubt started with an idea you thought might be true. No matter how much data you collect, there is always a chance that someone might contract hiv due to "chance" (all other reasons besides the vaccine). It simply means that there is not enough data to prefer one hypothesis over the al on hypothesis in hypothesis hypothesis testing ry of important terms in hypothesis virtual lab in tical inference activities using ti confused about conducting a hypothesis test? See some statistical analysis packages, to the role of statistics in seven key steps of data analysis while companies create data products specific to their own requirements and goals, some steps in the value chain are consistent across organizations. By gwen shapira the term “data scientist” evokes images of a single genius working alone, applying esoteric formulas to vast amounts of data in search of useful insights. Data analysis is not a goal in itself; the goal is to enable the business to make better decisions. Data scientists must build products that allow everyone in the organization to use data better, enabling data-driven decision making in every department and at every level.
The data value chain is captured in products that automatically collect, clean and analyze data, delivering information and predictions to executive dashboards or reports. Analysis runs automatically and continuously as new data arrives and the data scientists can work with the business on refining the models and improving prediction accuracy. While each company creates data products specific to its own requirements and goals, some of steps in the value chain are consistent across organizations: decide on the objectives: the first step of the data value chain must happen before there is data: the business unit has to decide on objectives for the data science teams. Since we are looking at data to drive decision-making, we need a measurable way to know if the business is advancing toward its goals. If there is nothing that can be changed, there can be no improvement regardless of how much data is collected and analyzed. Identifying the goals, metrics and levers early in the project provides the project with direction and avoids meaningless data analysis. More data—especially data from more diverse sources—enables finding better correlations, building better models and finding more actionable insights. Big data economics mean that while individual records are often useless, having every record available for analysis can provide real value. This is the most critical step in the data value chain—even with the best analysis, junk data will generate wrong results and mislead the business. However, schenectady has zip code 12345, so it is disproportionately represented in almost every customer profile database since consumers are often reluctant to enter their real details into online forms. Analyzing this data will result in erroneous conclusions unless the data analysts take steps to validate and clean the data. It is especially important that this step will scale, since having continuous data value chain requires that incoming data will get cleaned immediately and at very high rates. Data modeling: data scientists build models that correlate the data with the business outcomes and make recommendations regarding changes to the levers identified in the first step. This is where the unique expertise of data scientists becomes critical to business success—correlating the data and building models that predict business outcomes. Data scientists must have a strong background in statistics and machine learning to build scientifically accurate models and avoid the traps of meaningless correlations and models that are so reliant on existing data that their future predictions are useless. But statistical background is not enough; data scientists need to understand the business well enough that they will be able to recognize whether the results of the mathematical models are meaningful and relevant.
Grow a data science team: since data scientists are notoriously difficult to hire, it’s a good idea to build a data science team that allows those with an advanced degree in statistics to focus on data modeling and predictions, while others in the team—qualified infrastructure engineers, software developers and etl experts—build the necessary data collection infrastructure, data pipeline and data products that enable streaming the data through the models and displaying the results to the business in the form of reports and dashboards. These teams typically use large-scale data analysis platforms like hadoop to automate the data collection and analysis and run the entire process as a product. Optimize and repeat: the data value chain is a repeatable process and leads to continuous improvements, both to the business and to the data value chain itself. Based on the results of the model, the business will make changes to the driving levers and the data science team will measure the results. Based on the results, the business can decide on further action while the data science team improves its data collection, data cleanup and data models. The faster the business can repeat the process, the sooner it can make course corrections and get value out of the data. Ideally, after multiple iterations, the model will generate accurate predictions, the business will reach the predefined goals, and the resulting data value chain will be used for monitoring and reporting as everyone moves on to solve the next business challenge. Cloud applications & platform of use and wikipedia, the free to: navigation, of a series on atory data analysis • information ctive data ptive statistics • inferential tical graphics • analysis • munzner • ben shneiderman • john w. Tukey • edward tufte • fernanda viégas • hadley ation graphic chart • bar ram • t • pareto chart • area l chart • run -and-leaf display • multiple • unk • visual sion analysis • statistical ational cal analysis · analysis · /long-range potential · lennard-jones potential · yukawa potential · morse difference · finite element · boundary e boltzmann · riemann ative particle ed particle ation · gibbs sampling · metropolis algorithm. Body · v · ulam · von neumann · galerkin · analysis, also known as analysis of data or data analytics, is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. 1] in statistical applications data analysis can be divided into descriptive statistics, exploratory data analysis (eda), and confirmatory data analysis (cda). Eda focuses on discovering new features in the data and cda on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. Science process flowchart from "doing data science", cathy o'neil and rachel schutt, is refers to breaking a whole into its separate components for individual examination.
Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. John tukey defined data analysis in 1961 as: "procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data. Data is necessary as inputs to the analysis are specified based upon the requirements of those directing the analysis or customers who will use the finished product of the analysis. The general type of entity upon which the data will be collected is referred to as an experimental unit (e. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. Phases of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data initially obtained must be processed or organised for analysis. For instance, these may involve placing data into rows and columns in a table format (i. The need for data cleaning will arise from problems in the way that data is entered and stored. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data,[5] deduplication, and column segmentation. There are several types of data cleaning that depend on the type of data such as phone numbers, email addresses, employers etc. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spell checkers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. 9][10] the process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics such as the average or median may be generated to help understand the data.
Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data. Formulas or models called algorithms may be applied to the data to identify relationships among the variables, such as correlation or causation. In general terms, models may be developed to evaluate a particular variable in the data based on other variable(s) in the data, with some residual error depending on model accuracy (i. For example, regression analysis may be used to model whether a change in advertising (independent variable x) explains the variation in sales (dependent variable y). Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results. Data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy. Article: data the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. Determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays such as tables and charts to help communicate key messages contained in the data. Scatterplot illustrating correlation between two variables (inflation and unemployment) measured at points in stephen few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the -series: a single variable is captured over a period of time, such as the unemployment rate over a 10-year period. Also: problem jonathan koomey has recommended a series of best practices for understanding quantitative data. Problems into component parts by analyzing factors that led to the results, such as dupont analysis of return on equity. They may also analyze the distribution of the key variables to see how the individual values cluster around the illustration of the mece principle used for data consultants at mckinsey and company named a technique for breaking a quantitative problem down into its component parts called the mece principle. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false.
Hypothesis testing involves considering the likelihood of type i and type ii errors, which relate to whether the data supports accepting or rejecting the sion analysis may be used when the analyst is trying to determine the extent to which independent variable x affects dependent variable y (e. This is an attempt to model or fit an equation line or curve to the data, such that y is a function of ary condition analysis (nca) may be used when the analyst is trying to determine the extent to which independent variable x allows variable y (e. Whereas (multiple) regression analysis uses additive logic where each x-variable can produce the outcome and the x's can compensate for each other (they are sufficient but not necessary), necessary condition analysis (nca) uses necessity logic, where one or more x-variables allow the outcome to exist, but may not produce it (they are necessary but not sufficient). Each single necessary condition must be present and compensation is not ical activities of data users[edit]. May have particular data points of interest within a data set, as opposed to general messaging outlined above. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points. Some concrete conditions on attribute values, find data cases satisfying those data cases satisfy conditions {a, b, c... Derived a set of data cases, compute an aggregate numeric representation of those data is the value of aggregation function f over a given set s of data cases? Data cases possessing an extreme value of an attribute over its range within the data are the top/bottom n data cases with respect to attribute a? A set of data cases, rank them according to some ordinal is the sorted order of a set s of data cases according to their value of attribute a? Rank the cereals by a set of data cases and an attribute of interest, find the span of values within the is the range of values of attribute a in a set s of data cases? A set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the is the distribution of values of attribute a in a set s of data cases? Any anomalies within a given set of data cases with respect to a given relationship or expectation, e. A set of data cases, find clusters of similar attribute data cases in a set s of data cases are similar in value for attributes {x, y, z, ... A set of data cases and two attributes, determine useful relationships between the values of those is the correlation between attributes x and y over a given set s of data cases? A set of data cases, find contextual relevancy of the data to the data cases in a set s of data cases are relevant to the current users' context?
To effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data ing fact and opinion[edit]. Are entitled to your own opinion, but you are not entitled to your own patrick ive analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. In his book psychology of intelligence analysis, retired cia analyst richards heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques. Analysts apply a variety of techniques to address the various quantitative messages described in the section ts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysis, they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock. 21] the different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning, lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and ics and business intelligence[edit]. Article: ics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. It is a subset of business intelligence, which is a set of technologies and processes that use data to understand and analyze business performance. Activities of data visualization education, most educators have access to a data system for the purpose of analyzing student data. 23] these data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses. Section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a wikipedia l data analysis[edit]. Most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not for common-method choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.
Quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. During this analysis, one inspects the variances of the items and the scales, the cronbach's α of the scales, and the change in the cronbach's alpha when an item would be deleted from a scale[27]. Assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase. Should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in possible data distortions that should be checked are:Dropout (this should be identified during the initial data analysis phase). Nonresponse (whether this is random or not should be assessed during the initial data analysis phase). It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis characteristics of the data sample can be assessed by looking at:Basic statistics of important ations and -tabulations[31]. The final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are , the original plan for the main data analyses can and should be specified in more detail or order to do this, several decisions about the main data analyses can and should be made:In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method? The case of missing data: should one neglect or impute the missing data; which imputation technique should be used? The main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are atory data analysis should be interpreted carefully. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. There are two main ways of doing this:Cross-validation: by splitting the data in multiple parts we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as ivity analysis: a procedure to study the behavior of a system or model when global parameters are (systematically) varied. A database system endorsed by the united nations development group for monitoring and analyzing human – data mining framework in java with data mining oriented visualization – the konstanz information miner, a user friendly and comprehensive data analytics – a visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine – free software for scientific data – fortran/c data analysis framework developed at cern.
A programming language and software environment for statistical computing and – c++ data analysis framework developed at and pandas – python libraries for data ss ing (statistics). Presentation l signal case atory data inear subspace ay data t neighbor ear system pal component ured data analysis (statistics). Clean data in crm: the key to generate sales-ready leads and boost your revenue pool retrieved 29th july, 2016. William newman (1994) "a preliminary analysis of the products of hci research, using pro forma abstracts". How data systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Manual on presentation of data and control chart analysis, mnl 7a, isbn rs, john m. Data analysis: an introduction, sage publications inc, isbn /sematech (2008) handbook of statistical methods,Pyzdek, t, (2003). Data analysis: testing for association isbn ries: data analysisscientific methodparticle physicscomputational fields of studyhidden categories: wikipedia articles with gnd logged intalkcontributionscreate accountlog pagecontentsfeatured contentcurrent eventsrandom articledonate to wikipediawikipedia out wikipediacommunity portalrecent changescontact links hererelated changesupload filespecial pagespermanent linkpage informationwikidata itemcite this a bookdownload as pdfprintable version. A non-profit -flag-decore-flag-escore-flag-frcore-flag-gbcore-flag-uscore-icon-arrow-downcore-icon-arrow-leftcore-icon-arrow-long-downcore-icon-arrow-long-leftcore-icon-arrow-long-rightcore-icon-arrow-long-upcore-icon-arrow-rightcore-icon-arrow-upcore-icon-asteriskcore-icon-bulletcore-icon-chevron-downcore-icon-chevron-leftcore-icon-chevron-rightcore-icon-chevron-upcore-icon-closecore-icon-downloadcore-icon-facebookcore-icon-globecore-icon-instagramcore-icon-linkcore-icon-linkedincore-icon-listcore-icon-loadercore-icon-map-markercore-icon-pause-smallcore-icon-phonecore-icon-play-smallcore-icon-playcore-icon-pluscore-icon-printcore-icon-searchcore-icon-sharecore-icon-slidesharecore-icon-sortcore-icon-tickcore-icon-twittercore-icon-youtubecor listening and communications t audience atch for er stories from walmart, kellogg's, espn and -depth social data research and sector practice advice on using social your social atch for tabs, please #2: turkey gangs, mathematical athletes, and rainforest friday 2017 data: in uncertain times, how do consumers feel about spending? Insidebrandwatch: we choose our top 10 marketing campaigns of study: mental health in the study: mental health in the marketer of 2018: make data-backed decisions and find research answers atchknowledge g-edge social research and practical insights delivered straight to your for ’re now subscribed to our brandwatch knowledge it in your inbox every other iew: matt preschern on diversity, employee centricity and digital can healthcare services & pharmaceutical companies use social data? 16 best data visualization marketer of 2018: using social data to improve report: 25 things we learned analyzing billions of tweets. They have struggled to get usable insight from social data and deem social intelligence too costly to pursue as a research method because…. Do not have a clear process or method to segment and analyze social data and…. Have struggled to generate insight from the data because of the difficulty in interpreting the d of thinking through the analysis people have a tendency to rush into it to ‘see what happens…’ maybe you have done this yourself, i know i ucing the kitchen sink thing is that i quickly learned that by doing this it was the fast road to nowhere and a very big time-waster. Ve been analyzing social data for over ten hout this experience, both with and without tools at my disposal, i had unknowingly followed a wasn’t until earlier this year that i audited my proposals and project output, that i noticed the distinct steps that i take for social data analysis in marketing is the process i use with my own private clients and how i train my team to think about their analysis that process can be yours 1: define the start with the question you are trying to answer and what you want to be able to do with the insight once you have answering these two purpose questions you lay the foundations of how to approach the analysis and what the insight needs to do. You prepare yourself to focus on the data that you’ve been analyzing social data for a while then you’ll know that there is too much ‘white noise’.
This is extremely important because it helps you to break down the data into smaller relevant parts that you can analyze 3: keywords and are now ready to start getting the keywords, phrases and all know that social listening is dependent upon you creating a ‘search query’. Go back to your original question and think about the best way you can gather the data to answer that much data is confusing and it can easily lead you back down into a kitchen sink analysis. Too little data won’t 5: develop segmentation that you have the search query written and the data pulling through your tool, you now need to start segmenting the segments are related to how you deconstructed your question in step 2 and the phrases and keywords you collected at step 3. Go back and start to create your segmentation you don’t do this step properly you become reliant on ‘volume automation’ and you’ll never get the insight you are looking for because it’s 6: unknown social intelligence research is driven by naturally occurring social media conversations, you cannot account for every process i’ve outlined will help to prepare you, but you’ll always find a chunk of data that you’ve collected that doesn’t fit into any of your segmentation criteria. They are probably where the insight that you hadn’t considered is you are looking for in this data is to find a pattern and create a new segment or change up the segmentation criteria in the segments you had already ’re now finally ready for segmented the data, it will now be easier to work with and question that you are trying to answer will dictate the analysis. I advise looking for the context of the communication – discourse analysis on all the comments. Talk more about this step in my interactive pdf and get you to map out your 8: work doesn’t stop at analysis, you need to interpret what this means. You don’t measure the easy to get things, you prepare to answer the question ’ve deconstructed the question and segmented the data properly – this lets you know the volume of conversation in each ’ve analyzed the data to find out the context driving the conversation – this tells you the step is about putting all of this together and answering the original 9: additional are areas that have not been considered during the other eight steps that you can also example, who is talking or their immediate next step should be to review the searches and dashboards that you already have running, use this process to find out if you could be doing more and amend what you have you want more inside tips on how i analyze social data, you can sign-up for my updates here. Or if you want to go deeper you can request an invite to my membership community here, use the code social data to discover actionable insights and make informed jillian ney is the uk’s first dr of social media and digital behavioural scientist; helping businesses understand what is driving customer behavior to win more customers.
