Dar, Jhielo V.
BSBA MM 3-3
The six phases of data analytics are data discovery, data aggregation, data model
preparation, data model execution, communication of outcomes, and operationalization. These
six stages of the data analytics lifecycle are iterative, with backward and forward movement that
can often overlap. The data science project's main stakeholders play a variety of roles, including
industry. The metrics and outcomes of the data science project implementation can be described
by the business consumer or business analyst. The business analyst can be involved from the
beginning of the data initiative's value definition. From the specifications gathering stage
onwards, the project sponsor discovers the business conundrum and becomes active. The project
manager ensures that the project's deliverables are of high quality. The project manager ensures
that the final data product's deliverables are of high quality, and that the project is completed on
schedule and on budget, using all of the project's resources. The business intelligence analyst is
the organization's professional stakeholder when it comes to creating and identifying dashboards
and key performance metrics. The database administrator sets up the database and manages it.
The database administrator sets up the database and gives the data analytics team access to it,
including granting authorizations. With SQL and NoSQL queries for data ingestion and analysis,
the data engineer is the subject matter expert.
Phase 1: Data Discovery and Formation
The first stage entails laying out the possible uses and requirements of data, such as
where the data comes from, what story you want your data to tell, and how your company profits
from the data. Essentially, as a data analyst, you'll need to concentrate on business data criteria
rather than data itself. Additionally, your work also includes assessing the tools and systems that
are necessary to read, organize, and process all the incoming data. The structuring of the business
issue in the form of an analytics task, as well as the formulation of initial hypotheses (IHs) to test
and begin learning the data, are both important activities in this process. The goals that are drawn
in this stage are then used to guide the subsequent stages.
Phase 2: Data Preparation and Processing
This stage consists of everything that has anything to do with data. In phase 2, the attention of
experts moves from business requirements to information requirements. Collecting, processing,
and cleaning the accumulated data are all part of the data preparation and processing phase.
One of the most important aspects of this step is ensuring that the data you need is currently
available for processing. Collecting useful knowledge and moving on with the data analytics
lifecycle in a business ecosystem are the first steps in the data planning process. The data are
collected using these methods;
Data Acquisition: Accumulating information from external sources.
Data Entry: Formulating recent data points using digital systems or manual data entry
techniques within the enterprise.
Signal Reception: Capturing information from digital devices, such as control systems and
the Internet of Things.
Phase 3: Design a Model
After mapping out your business goals and collecting a glut of data (structured, unstructured, or
semi-structured), it is time to build a model that utilizes the data to achieve the goal.
There are several techniques available to load data into the system and start studying it:
ETL (Extract, Transform, and Load) transforms the data first using a set of business rules, before
loading it into a sandbox.
ELT (Extract, Load, and Transform) first loads raw data into the sandbox and then transform it.
ETLT (Extract, Transform, Load, Transform) is a mixture; it has two transformation levels.
This step also involves teamwork to evaluate the processes, strategies, and workflow that will be
used to build the model in the next process. The process of constructing a model begins with
determining the relationship between data points in order to pick key variables and, finally, a
suitable model..
Phase 4: Model Building
The development of data sets for research, training, and production purposes is part of the data
analytics architecture step. The data analytics experts build and operate the model that they
created in the previous phase with great care. For constructing and implementing the model,
they use tools and techniques such as decision trees, regression techniques (logistic regression),
and neural networks. The experts also perform a trial run of the model to observe if the model
corresponds to the datasets.
Phase 5: Result Communication and Publication
Now is the time to check if those criteria are met by the tests you have run in the previous
phase. The communication step starts with a collaboration with major stakeholders to determine
if the project results are a success or failure. The project team is required to identify the key
findings of the analysis, measure the business value associated with the result, and produce a
narrative to summarize and convey the results to the stakeholders.
Phase 6: Measuring of Effectiveness
As your data analytics lifecycle draws to a conclusion, the final step is to provide a detailed
report with key findings, coding, briefings, technical papers/ documents to the stakeholders.
Additionally, to measure the analysis’s effectiveness, the data is moved to a live environment
from the sandbox and monitored to observe if the results match the expected business goal. If
the findings are as per the objective, the reports and the results are finalized. However, suppose
the outcome deviates from the intent set out in phase 1then. You can move backward in the data
analytics lifecycle to any of the previous phases to change your input and get a different output.
2. Forecasting
Forecasting is the art of predicting what will happen in the future by taking into account past
and current events. Essentially, it is a decision-making method that examines historical data and
patterns to assist companies in coping with the effects of potential uncertainty. It's a strategy
tool that helps companies map out their next steps and build budgets that will help them
succeed. It is a planning tool that enables businesses to chart their next moves and create budgets
that will hopefully cover whatever uncertainties may occur. Forecasting is the method of
predicting or projecting future events using historical and current data, most generally by trend
analysis. "Guessing" isn't going to cut it. We may, for example, use forecasting to estimate how
many consumer calls Phil, our product evangelist, will receive in the coming days. Or how many
product demonstrations he'll do in the coming week.
There are two methods in conducting forecasting, qualitative and quantitative method.
Qualitative forecasting, also known as the judgmental approach, produces subjective results
because it is based on experts' or forecasters' personal opinions. Forecasts are often skewed because
they are based on an expert's expertise, intuition, and experience rather than evidence, rendering
the process non-mathematical. One example is when a person forecasts the outcome of a finals
game in the NBA, which, of course, is based more on personal motivation and interest. The
weakness of such a method is that it can be inaccurate. On the other hand, Forecasting
quantitatively is a statistical process that ensures consistency and objectivity. It avoids relying on
opinion and intuition for performance, instead relying on vast quantities of data and statistics that
are interpreted.
Forecasters need to follow a careful process in order to yield accurate results. Here are some
steps in the process:
The first step in the process is to lay the groundwork for the company's condition
investigation and to determine where the company stands in the market. The second part of
forecasting, which is based on the first step's investigation, entails predicting future conditions of
the market in which the company operates, as well as projecting and evaluating how the
company will perform. The third step entails reviewing previous predictions and comparing them
to the actual events that occurred in the company. The variations between previous findings and
current predictions are investigated, as well as the explanations for the deviations. During the
review process, every step is checked, and refinements and modifications are made.
3.
The science of learning from data is known as statistics. Statistical information aids in the
collection of data, the application of correct analyses, and the successful presentation of results.
Statistics is an important part of how we make scientific discoveries, make data-driven decisions,
and make forecasts. Statistics helps you to get a better understanding of a topic. Statisticians
provide crucial guidance in the development of reliable assessments and forecasts. Statistical
experts may assist prosecutors in avoiding a number of methodological pitfalls. When analysts
correctly apply statistical methods, they obtain reliable results. In reality, statistical studies
account for the results' uncertainty and error. Statisticians make sure that all facets of a study
are covered. Statisticians ensure that all aspects of a study follow the appropriate methods to
produce trustworthy results. These methods include: Producing reliable data. Analyzing the data
appropriately. Drawing reasonable conclusions. Statistical analyses are used in almost every area
to make sense of the massive amounts of data available. Even if statistics isn't your primary field
of research, it will help you make a difference in whatever field you want. You'll almost certainly
need a working knowledge of statistical methods to both generate new discoveries in your field
and comprehend the work of others.
Sources:
https://www.upgrad.com/blog/data-analytics-lifecycle/
https://corporatefinanceinstitute.com/resources/knowledge/finance/forecasting/