The central thrust behind every digital transformation and data modernization is to improve the volume, quality, and accessibility of data within the organization, and to dramatically improve the ability to safety and securely engage with that data. High quality data enables organizations to address complex customer initiatives and inform strategic decisions.Poor quality data can have a direct and damaging impact on the bottom line.
Defining a data strategy: An essential component of your digital transformation journey
All organizations engage with, operate on and leverage data every day across a variety of business functions. Those organizations that take a holistic approach to adopting an enterprise-grade data strategy are able to optimize their technology investments and lower their costs.,
Organizations that want a smooth transition to becoming data driven need a plan for advancing their digital transformation journey and treating data as a corporate asset. Creating a data strategy is the first step toward defining and enabling such a plan.
The data strategy vision
All organizations make decisions about how they engage with, operate on and leverage their data — whether at an enterprise or project level. Companies that form a holistic point of view in adopting an enterprise-grade data strategy are well positioned to optimize their technology investments and lower their costs. Such a strategy treats data as an asset from which valuable insights can be derived. These insights can be used to gain a competitive advantage by being integrated into business operations.
Organizations that want a smooth transition to becoming data driven — aligning operational decisions to the systematic (and automatic as much as possible) interpretation of data — need a plan for advancing their digital transformation journey and treating data as a corporate asset. Creating a data strategy is the first step toward enabling such a plan and increasing the organization’s Analytics IQ. This term refers to an organization’s ability to deploy advanced analytics at every point of interaction — human as well as machine — to continuously improve decision-making quality and accuracy.
A data strategy ensures that all data initiatives follow a common method and structure that is repeatable. This uniformity enables efficient communication throughout the enterprise for rationalizing and defining all solution designs that leverage data in some manner.
Many organizations fail to prioritize defining a data strategy on the grounds that it’s either a case of “boiling the ocean” or else an “infinity project” that will deliver little value. In both cases, they’re incorrect. Creating a data strategy is both achievable and valuable. It’s also an essential component of any organization’s digital transformation journey.
Companies that embrace the constructs of a data strategy often define dedicated roles to own these strategies and policies. This ranges from augmenting executive staff and IT staff with roles such as chief data officer and chief data strategist, respectively, to expanding the responsibilities of traditional enterprise data architects.
91 percent of organizations have not yet reached a "transformational" level of maturity in data and analytics, despite this area being a number one investment priority for CIOs in recent years.
As automation, machine learning, and cloud rise to the forefront, it is critical to implement the right data quality tools and a comprehensive strategy that delivers results in a timely manner. Smartek21 offers a wide variety of data management services, including:
We design data governance strategies and create policies describing user roles, rights and responsibilities as well as data-related standards and metrics. Such strategies aim to provide a transparent enterprise-wide approach to capturing, storing, processing and accessing data.
Data quality management
Many businesses don’t trust their data, and our job is to ensure that yours is not one of them. Low quality data can take many forms: duplicated, incomplete, erroneous or obsolete data. We run data quality assurance and data cleaning activities to fight them all and make your insights genuinely true to life.
Master and metadata management
We create a clear strategy for enterprise-wide master and metadata management. We assess all external and internal data sources as well as relevant existing technologies to come up with standards and metrics to track your master and metadata quality.
The reports built on disintegrated data cannot show a full and consistent picture. We know how to unite flows from disparate data sources. Keeping in mind your data types, we can come up with a relevant BI or big data architecture, as well as a data warehouse or a data lake design (or both, if needed). This will make your reporting more comprehensive.
Before actually performing data migration, we carry out an assessment to identify sensitive and critical data. Considering the results, we devise a data migration plan. To speed up the migration process and minimize mistakes, we make the process as automated as possible. After migration is over, we verify its results to ensure that no data was lost.
We recommend additional sources and new data to get extra insights, make your decisions more informed and predictions more accurate. We also actually make this external data an integral part of your existing data sets.
Regardless of whether your data is structured or not, we can retrieve it from multiple sources, load it into a data warehouse or a data lake and transform it according to your needs. We can set up automated web data extraction procedures to get valuable insights from comments and posts in social networks, real estate listings, competitor prices, etc.
To ensure that your data is secure and only authorized users have access to it, we analyze your business specifics and apply best practices to review your current approach to enterprise information management. We evaluate the capacity and reliability of the existing architecture and technology stack as well as suggest ways to improve the security of your solution.
Data architecture audit and implementation
We can audit your as-is architecture to check whether it complies well with data management procedures. In case you need to implement a BI or big data solution from scratch with regard to data management techniques, you can assign us to this task, too. We have 14 and 6 years of experience in these domains, correspondingly. We can also run a comprehensive health check of your data warehouse, its security and performance.
With all these services, we ensure that we
Choose an optimal architecture and platforms from multiple options
We help our customers not to get lost among multiple possible options: cloud-based or in-house solution, a required platform or framework able to solve the tasks (for instance, Hadoop or Spark). We tailor our suggestions of architecture and platforms to our customers’ needs. For each option, we describe all pros and cons in detail and recommend the best option.
Optimize total cost of ownership
We are laser-focused on the possibilities to optimize an existing architecture to reduce customer’s costs (such as the costs of cloud services, software license, software maintenance, data acquisition). We offer alternative options that bring both extra benefits and reduced costs.
Integrate various platforms and services
A big data solution always combines multiple components, as there are multiple data sources. We provide our customers with a sufficient stack of compatible platforms and services needed to satisfy the business needs.