Archive for category: Big Data

Effective data visualization tells the user, at a glance, everything that is necessary to know. Hence the dashboards need to be well designed so as to give the users the right picture to analyze data, track performance and make informed decisions. Therefore, it is important to know the dashboard best practices so that users have access to what they really need.

Designing an effective dashboard

Planning is essential for effective dashboards. It is important to involve the business users when mapping the delivery requirements. It is a good idea to host a planning interview with the business users where the key business metrics that need to be tracked are identified. In addition, the business users should share the frequency of information updating. The data visualization team should be aware even before the start of the project about whether the metrics definitions are the same across the business unit and also whether the data level access rights etc. All of this will help in the creation of dashboards that are both user friendly and deliver the value promised.

It is important that the users are not overwhelmed with information. Depending on the user roles, the information access should be streamlined. It is most often enough to provide high level metrics that highlight the business process performance. In most cases, the business users require summarized information on the dashboard screen and this should be visualized in such a manner that it is easy to understand at a glance. If interested, the users should be able to drill down further information on the metrics shown.

Best practices for data visualization:

1. There should be around 4 to 6 metrics on a single dashboard page. The metrics pertaining to a specific business unit need to be on a specific tab or page. This helps provide easy access and understanding.
2. The ability to drill down should be a maximum of up to three levels. If the user needs to drill down further, they can always take the assistance of a data analyst or the IT team when needed.
3. Charts, tables, graphs etc. should be utilized for data visualization. This helps in easy interpretation of the data. It is better to not go for pie charts. Bar charts are a better option to showcase differences to the users. The usage of gauges also needs to be restricted, as they do not provide too much information and at the same time occupy a lot of screen space.
4. Dashboards need to be interactive; this would help the users access information easily and also customize the data views. Dashboards with filters help users to maneuver the data scope so as to meet their specific needs. Alerts help in capturing the user’s attention.

Data visualization mistakes to avoid

It is important that the users are not overwhelmed with information. Depending on the user roles, the information access should be streamlined. It is most often enough to provide high level metrics that highlight the business process performance. In most cases, the business users require summarized information on the dashboard screen and this should be visualized in such a manner that it is easy to understand at a glance. If interested, the users should be able to drill down further information on the metrics shown.
The graphs and charts should not be poorly designed. Considerable thought should be put into creating graphs and charts which boost viewer comprehension. Garish colors should be avoided.
Using metrics that the broader audience would not understand should be avoided. Easier concepts and metrics would help in keeping the dashboard reader friendly.

Keeping these simple tips in mind while working on data visualization and dashboard design would help in coming up with solutions that are coveted by users and help in business decision making.

For a company, initiating into big data is a huge step, something that has the potential to transform the face of any business in the market for the better. With the ongoing expansion and adoption of analytics by a majority of business houses of various industries and the availability of an even larger amount of literature on big data in the virtual world, it is easy to get confused or lost by erroneous perception created due to a careless assessment.

Every business requires a different business module which works specifically and uniquely for that particular company. Processing big data requires unique strategic analysis that varies from one company to other depending on its market status, customer demographics and extent of social media presence, among others. Getting gripped by misguided notions can lead to unfulfilling big data outputs causing great disappointment in the big data project. However, by avoiding 5 typical mistakes in the process you can ensure the actionable insights and results that big data ambitiously promises.

Selecting wrong KPIs

Key performance indicators are aplenty in the social media realm. With big data, you have at your disposal an array of KPIs like sales growth, product performance, customer attrition, inventory, turnover etc. to scrutinize. Due to this, there are chances that a company might go wrong in focusing on the issue-specific KPI and get incorrect results. Here, the question of exactly what to measure is important to identify the kind of KPIs to use and analyze. It is therefore dependent on the objectives of the business plan and the expected outcome.
Two mistakes can lead to incorrect analysis:

1. Having all the data in hand but asking incorrect questions.
2. Asking the correct questions but not looking into appropriate data for answers.

To avoid these, there is a simple 3-step process that you can follow:

1. 1. Formulate the right questions by keeping the business objectives in mind;
   2. Sift through all the data and extract relevant bits that will give actionable insights; and finally
   3. Analyze them to model strategic solutions.

No effort to unify dispersed data

The stagnant data silos can impede the data management and data integrity efforts of an organization. It can hamper productivity by coming in the way of organized data utilization and processing. The repository data is mostly unused, lying in some IT nooks that do not participate in the integrated voice of customer analysis. Ignoring internal data, which exists in several legacy systems and other internal data sources like surveys, call center notes, CRM etc. is a huge misstep.
For a complete data integration, it is important to build a 360-degree Voice of Customer Intelligence. It enables the integration of both the structured and the unstructured data which gives a complete view of the ecosystem under study.

For effective intelligence, this integration is needed.

Lack of inter-departmental synergy

The ‘Silo Mentality’ as defined by the Business Dictionary is a mindset present when certain departments or sectors do not wish to share information with others in the same company. This type of mentality will reduce efficiency in the overall operation, reduce morale, and may contribute to the demise of a productive company culture.

Any organization inevitably requires a coordinated working of different departments to reap results, due to which the presence of organizational silos proves to be a highly detrimental barrier to deal with. It’s a major roadblock for a data-driven business.

Tackling this, however, is easier than believed. The CMO-CIO synergy needs to be strengthened as the first major step towards breaking organizational silos. More often than not, what we see is a huge gap between the IT and marketing department, causing the lack of understanding and disorganized, haphazard flow of information between the two. Maximum efficiency can only be ensured by bridging the gap between them.
Other simple steps like:

1. Bringing on board a Chief Data Officer,
2. Enhancing Marketing-Technology synergy,
3. Focusing on critical/relevant data etc. can help in breaking the organizational silos and maximizing a company’s productivity.

Opting for free/cheap big data tools

It’s true enough that the market today is full of free software providing easily accessible analytics to companies and promising complete integrated solutions to business problems. While not debunking their utility entirely, it is very important to understand that their functions serve only a limited purpose and in no way are enough to provide a wholesome analytics solution for the company’s specific requirements. Falling for the ‘free service’ tag can cost the organization its security and even present a possible threat of losing some of their precious data. This is because most open source software aren’t fully secured and are not known to scale well to the mammoth data analytics requirements.

A company, therefore, must firstly understand and define its objectives and requirements and then accordingly go for a solution provider who offers them the customized services.

Letting the big data solution drive the results instead of the business objective

All the big data and analytics center on business objectives at the end of the day. The extraction, processing and analyzing are all done with the intention of solving business issues which invariably lie at the core of all the ventures of the company, including the most technologically demanding big data project. The CIO must streamline all his IT workflows with the marketing goals. This alignment between technology and business is of utmost importance because excess focus on only the technological front without periodic business intervention and review would produce incongruous data and hinder productivity.

Using customer  intelligence can ensure high ROI and tangible outputs. A synergy of data scientists, consultants, processes and technologists is a must for an efficient working and delivery of customized, problem-specific solutions to business issues.